@misc{zhangWhyAreCountries2026,
	title = {Why {Are} {Some} {Countries} {More} {Politically} {Fragmented} {Online} {Than} {Others}?},
	url = {http://arxiv.org/abs/2601.05093},
	doi = {10.48550/arXiv.2601.05093},
	abstract = {Online political divisions, such as fragmentation or polarization, are a growing global concern that can foster radicalization and hinder democratic cooperation; however, not all divisions are detrimental, some reflect pluralism and healthy diversity of opinion in a democracy. While prior research has predominantly focused on polarization in the United States, there remains a limited body of research on political divides in multiparty systems, and no universal method for comparing fragmentation across countries. Moreover, cross-country comparison is rare. This study first develops a novel measure of structural political fragmentation built on multi-scale community detection and the effective branching factor. Using a dataset of 18,325 political influencers from Brazil, Spain, and the United States, we assess online fragmentation in their Twitter/X co-following networks. We compare the fragmentation of the three countries, as well as the ideological groups within each. We further investigate factors associated with the level of fragmentation in each country. We find that political fragmentation differs across countries and is asymmetric between ideological groups. Brazil is the most fragmented, with higher fragmentation among the left-wing group, while Spain and the United States exhibit similar overall levels, with the left more fragmented in Spain and the right more fragmented in the United States. Additionally, we find that social identity plays a central role in political fragmentation. A strong alignment between ideological and social identities, with minimal overlap between ideologies, tends to promote greater integration and reduce fragmentation. Our findings provide explanations for cross-national and ideological differences in political fragmentation.},
	urldate = {2026-02-25},
	publisher = {arXiv},
	author = {Zhang, Yuan and Castro, Laia and Esser, Frank and Bovet, Alexandre},
	month = jan,
	year = {2026},
	note = {arXiv:2601.05093 [cs]},
	keywords = {computational social science, network science, polarization, social media},
}

Online political divisions, such as fragmentation or polarization, are a growing global concern that can foster radicalization and hinder democratic cooperation; however, not all divisions are detrimental, some reflect pluralism and healthy diversity of opinion in a democracy. While prior research has predominantly focused on polarization in the United States, there remains a limited body of research on political divides in multiparty systems, and no universal method for comparing fragmentation across countries. Moreover, cross-country comparison is rare. This study first develops a novel measure of structural political fragmentation built on multi-scale community detection and the effective branching factor. Using a dataset of 18,325 political influencers from Brazil, Spain, and the United States, we assess online fragmentation in their Twitter/X co-following networks. We compare the fragmentation of the three countries, as well as the ideological groups within each. We further investigate factors associated with the level of fragmentation in each country. We find that political fragmentation differs across countries and is asymmetric between ideological groups. Brazil is the most fragmented, with higher fragmentation among the left-wing group, while Spain and the United States exhibit similar overall levels, with the left more fragmented in Spain and the right more fragmented in the United States. Additionally, we find that social identity plays a central role in political fragmentation. A strong alignment between ideological and social identities, with minimal overlap between ideologies, tends to promote greater integration and reduce fragmentation. Our findings provide explanations for cross-national and ideological differences in political fragmentation.

@misc{koovelyGeneratingTemporalNetworks2026,
	title = {Generating temporal networks with the {Ascona} model},
	url = {http://arxiv.org/abs/2512.16972},
	doi = {10.48550/arXiv.2512.16972},
	abstract = {We introduce a queueing-based sampling framework for continuous-time temporal networks. We focus on a Markovian parametrization in which link start times follow a homogeneous Poisson process and link durations are exponentially distributed. We derive stochastic properties of the resulting link streams and exploit them to generate synthetic temporal networks with controllable smoothness and prescribed event patterns, relevant for the validation and interpretation of methods for community, scale, change-point, and periodicity detection. By coupling this temporal mechanism with block-structured endpoint distributions, we obtain a continuous-time analogue of stochastic block models. We also discuss extensions of the framework, including discrete-time and instantaneous-contact limits.},
	urldate = {2026-03-13},
	publisher = {arXiv},
	author = {Koovely, Samuel},
	month = feb,
	year = {2026},
	note = {arXiv:2512.16972 [physics]},
	keywords = {network science, temporal networks},
}

We introduce a queueing-based sampling framework for continuous-time temporal networks. We focus on a Markovian parametrization in which link start times follow a homogeneous Poisson process and link durations are exponentially distributed. We derive stochastic properties of the resulting link streams and exploit them to generate synthetic temporal networks with controllable smoothness and prescribed event patterns, relevant for the validation and interpretation of methods for community, scale, change-point, and periodicity detection. By coupling this temporal mechanism with block-structured endpoint distributions, we obtain a continuous-time analogue of stochastic block models. We also discuss extensions of the framework, including discrete-time and instantaneous-contact limits.

@article{candelloneNegativeTiesHighlight2025,
	title = {Negative ties highlight hidden extremes in social media polarization},
	volume = {13},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2050-1242, 2050-1250},
	url = {https://www.cambridge.org/core/product/identifier/S2050124225100064/type/journal_article},
	doi = {10.1017/nws.2025.10006},
	abstract = {Human interactions in the online world comprise a combination of positive and negative exchanges. These diverse interactions can be captured using signed network representations, where edges take positive or negative weights to indicate the sentiment of the interaction between individuals. Signed networks offer valuable insights into online political polarization by capturing antagonistic interactions and ideological divides on social media platforms. This study analyzes polarization on Menéame, a Spanish social media platform that facilitates engagement with news stories through comments and voting. Using a dualmethod approach—Signed Hamiltonian Eigenvector Embedding for Proximity for signed networks and Correspondence Analysis for unsigned networks—we investigate how including negative ties enhances the understanding of structural polarization levels across different conversation topics on the platform. While the unsigned Menéame network effectively delineates ideological communities, only by incorporating negative ties can we identify ideologically extreme users who engage in antagonistic behaviors: without them, the most extreme users remain indistinguishable from their less confrontational ideological peers.},
	language = {en},
	urldate = {2025-09-02},
	journal = {Network Science},
	author = {Candellone, E. and Babul, S. A. and Togay, Ö. and Bovet, A. and Garcia-Bernardo, J.},
	year = {2025},
	keywords = {computational social science, network science, polarization, social media},
	pages = {e10},
}

Human interactions in the online world comprise a combination of positive and negative exchanges. These diverse interactions can be captured using signed network representations, where edges take positive or negative weights to indicate the sentiment of the interaction between individuals. Signed networks offer valuable insights into online political polarization by capturing antagonistic interactions and ideological divides on social media platforms. This study analyzes polarization on Menéame, a Spanish social media platform that facilitates engagement with news stories through comments and voting. Using a dualmethod approach—Signed Hamiltonian Eigenvector Embedding for Proximity for signed networks and Correspondence Analysis for unsigned networks—we investigate how including negative ties enhances the understanding of structural polarization levels across different conversation topics on the platform. While the unsigned Menéame network effectively delineates ideological communities, only by incorporating negative ties can we identify ideologically extreme users who engage in antagonistic behaviors: without them, the most extreme users remain indistinguishable from their less confrontational ideological peers.

@article{quelleBlueskyNetworkTopology2025,
	title = {Bluesky: {Network} topology, polarization, and algorithmic curation},
	volume = {20},
	copyright = {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License},
	issn = {1932-6203},
	shorttitle = {Bluesky},
	url = {https://dx.plos.org/10.1371/journal.pone.0318034},
	doi = {10.1371/journal.pone.0318034},
	abstract = {Bluesky is a nascent “Twitter-like” and decentralized social media network with novel features and unprecedented data access. This paper provides a characterization of its interaction network, studying the political leaning, polarization, network structure, and algorithmic curation mechanisms of five million users. The dataset spans from the website’s first release in February of 2023 to May of 2024. We investigate the replies, likes, reposts, and follows layers of the Bluesky network. We find that all networks are characterized by heavy-tailed distributions, high clustering, and short connection paths, similar to other larger social networks. BlueSky introduced feeds—algorithmic content recommenders created for and by users. We analyze all feeds and find that while a large number of custom feeds have been created, users’ uptake of them appears to be limited. We analyze the hyperlinks shared by BlueSky’s users and find no evidence of polarization in terms of the political leaning of the news sources they share. They share predominantly leftcenter news sources and little to no links associated with questionable news sources. In contrast to the homogeneous political ideology, we find significant issues-based divergence by studying opinions related to the Israel-Palestine conflict. Two clear homophilic clusters emerge: Pro-Palestinian voices outnumber pro-Israeli users, and the proportion has increased. We conclude by claiming that Bluesky—for all its novel features—is very similar in its network structure to existing and larger social media sites and provides unprecedented research opportunities for social scientists, network scientists, and political scientists alike.},
	language = {en},
	number = {2},
	urldate = {2025-02-27},
	journal = {PLOS ONE},
	author = {Quelle, Dorian and Bovet, Alexandre},
	editor = {Pierri, Francesco},
	month = feb,
	year = {2025},
	keywords = {computational social science, network science, polarization, social media},
	pages = {e0318034},
}

Bluesky is a nascent “Twitter-like” and decentralized social media network with novel features and unprecedented data access. This paper provides a characterization of its interaction network, studying the political leaning, polarization, network structure, and algorithmic curation mechanisms of five million users. The dataset spans from the website’s first release in February of 2023 to May of 2024. We investigate the replies, likes, reposts, and follows layers of the Bluesky network. We find that all networks are characterized by heavy-tailed distributions, high clustering, and short connection paths, similar to other larger social networks. BlueSky introduced feeds—algorithmic content recommenders created for and by users. We analyze all feeds and find that while a large number of custom feeds have been created, users’ uptake of them appears to be limited. We analyze the hyperlinks shared by BlueSky’s users and find no evidence of polarization in terms of the political leaning of the news sources they share. They share predominantly leftcenter news sources and little to no links associated with questionable news sources. In contrast to the homogeneous political ideology, we find significant issues-based divergence by studying opinions related to the Israel-Palestine conflict. Two clear homophilic clusters emerge: Pro-Palestinian voices outnumber pro-Israeli users, and the proportion has increased. We conclude by claiming that Bluesky—for all its novel features—is very similar in its network structure to existing and larger social media sites and provides unprecedented research opportunities for social scientists, network scientists, and political scientists alike.

@misc{salloumPoliticsPolarizationBluesky2025,
	title = {Politics and polarization on {Bluesky}},
	url = {http://arxiv.org/abs/2506.03443},
	doi = {10.48550/arXiv.2506.03443},
	abstract = {Online political discourse is increasingly shaped not by a few dominant platforms but by a fragmented ecosystem of social media spaces, each with its own user base, target audience, and algorithmic mediation of discussion. Such fragmentation may fundamentally change how polarization manifests online. In this study, we investigate the characteristics of political discourse and polarization on the emerging social media site Bluesky. We collect all activity on the platform between December 2024 and May 2025 to map out the platform's political topic landscape and detect distinct polarization patterns. Our comprehensive data collection allows us to employ a data-driven methodology for identifying political themes, classifying user stances, and measuring both structural and content-based polarization across key topics raised in English-language discussions. Our analysis reveals that approximately 13\% of Bluesky posts engage with political content, with prominent topics including international conflicts, U.S. politics, and socio-technological debates. We find high levels of structural polarization across several salient political topics. However, the most polarized topics are also highly imbalanced in the numbers of users on opposing sides, with the smaller group consisting of only 1-2\% of the users. While discussions in Bluesky echo familiar political narratives and polarization trends, the platform exhibits a more politically homogeneous user base than was typical prior to the current wave of platform fragmentation.},
	urldate = {2026-02-25},
	publisher = {arXiv},
	author = {Salloum, Ali and Quelle, Dorian and Iannucci, Letizia and Bovet, Alexandre and Kivelä, Mikko},
	month = jun,
	year = {2025},
	note = {arXiv:2506.03443 [cs]},
	keywords = {computational social science, network science, polarization, social media},
}

Online political discourse is increasingly shaped not by a few dominant platforms but by a fragmented ecosystem of social media spaces, each with its own user base, target audience, and algorithmic mediation of discussion. Such fragmentation may fundamentally change how polarization manifests online. In this study, we investigate the characteristics of political discourse and polarization on the emerging social media site Bluesky. We collect all activity on the platform between December 2024 and May 2025 to map out the platform's political topic landscape and detect distinct polarization patterns. Our comprehensive data collection allows us to employ a data-driven methodology for identifying political themes, classifying user stances, and measuring both structural and content-based polarization across key topics raised in English-language discussions. Our analysis reveals that approximately 13% of Bluesky posts engage with political content, with prominent topics including international conflicts, U.S. politics, and socio-technological debates. We find high levels of structural polarization across several salient political topics. However, the most polarized topics are also highly imbalanced in the numbers of users on opposing sides, with the smaller group consisting of only 1-2% of the users. While discussions in Bluesky echo familiar political narratives and polarization trends, the platform exhibits a more politically homogeneous user base than was typical prior to the current wave of platform fragmentation.

@article{zhangMultilevelNetworkApproach2025,
	title = {A multilevel network approach to revealing patterns of online political selective exposure},
	volume = {20},
	copyright = {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0332663},
	doi = {10.1371/journal.pone.0332663},
	abstract = {Selective exposure, individuals’ inclination to seek out information that supports their beliefs while avoiding information that contradicts them, plays an important role in the emergence of polarization and echo chambers. In the political domain, selective exposure is usually measured on a left-right ideology scale, ignoring finer details. To bridge the gap, this work introduces a multilevel analysis framework based on a multi-scale community detection approach. To test this approach, we combine survey and Twitter/X data collected during the 2022 Brazilian Presidential Election and investigate selective exposure patterns among survey respondents in their choices of whom to follow. We construct a bipartite network connecting survey respondents with political influencers and project it onto the influencer nodes. Applying multi-scale community detection to this projection uncovers a hierarchical clustering of political influencers, where each cluster is more frequently co-followed by a specific subgroup of survey respondents compared to others. Different indices of selective exposure, such as Community Overlap, Identity Diversity, Information Diversity, Structural Integration, and Connectivity Inequality, suggest that the characteristics of the influencer communities engaged by survey respondents vary with the level of community resolution. This finding indicates that online political selective exposure exhibits a more complex structure than a mere left-right dichotomy. Moreover, depending on the resolution level we consider, we find different associations between network indices of exposure patterns and 189 individual attributes of the survey respondents. For example, at finer levels, survey respondents’ Community Overlap is associated with several factors, such as ideological position, demographics, news consumption frequency, and incivility perception. In comparison, only their ideological position is a significant factor at coarser levels. Our work demonstrates that measuring selective exposure at a single level, such as left and right, misses important information necessary to capture this phenomenon correctly.},
	language = {en},
	number = {9},
	urldate = {2025-09-23},
	journal = {PLOS One},
	author = {Zhang, Yuan and Castro, Laia and Esser, Frank and Bovet, Alexandre},
	editor = {Rivas-de-Roca, Rubén},
	month = sep,
	year = {2025},
	keywords = {computational social science, network science, polarization, social media},
	pages = {e0332663},
}

Selective exposure, individuals’ inclination to seek out information that supports their beliefs while avoiding information that contradicts them, plays an important role in the emergence of polarization and echo chambers. In the political domain, selective exposure is usually measured on a left-right ideology scale, ignoring finer details. To bridge the gap, this work introduces a multilevel analysis framework based on a multi-scale community detection approach. To test this approach, we combine survey and Twitter/X data collected during the 2022 Brazilian Presidential Election and investigate selective exposure patterns among survey respondents in their choices of whom to follow. We construct a bipartite network connecting survey respondents with political influencers and project it onto the influencer nodes. Applying multi-scale community detection to this projection uncovers a hierarchical clustering of political influencers, where each cluster is more frequently co-followed by a specific subgroup of survey respondents compared to others. Different indices of selective exposure, such as Community Overlap, Identity Diversity, Information Diversity, Structural Integration, and Connectivity Inequality, suggest that the characteristics of the influencer communities engaged by survey respondents vary with the level of community resolution. This finding indicates that online political selective exposure exhibits a more complex structure than a mere left-right dichotomy. Moreover, depending on the resolution level we consider, we find different associations between network indices of exposure patterns and 189 individual attributes of the survey respondents. For example, at finer levels, survey respondents’ Community Overlap is associated with several factors, such as ideological position, demographics, news consumption frequency, and incivility perception. In comparison, only their ideological position is a significant factor at coarser levels. Our work demonstrates that measuring selective exposure at a single level, such as left and right, misses important information necessary to capture this phenomenon correctly.

@article{quelleLostTranslationUsing2025,
	title = {Lost in translation: using global fact-checks to measure multilingual misinformation prevalence, spread, and evolution},
	volume = {14},
	copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License},
	issn = {2193-1127},
	shorttitle = {Lost in translation},
	url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-025-00520-6},
	doi = {10.1140/epjds/s13688-025-00520-6},
	abstract = {Abstract
            Misinformation and disinformation are growing threats in the digital age, affecting people across languages and borders. However, no research has investigated the prevalence of multilingual misinformation and quantified the extent to which misinformation diffuses across languages. This paper investigates the prevalence and dynamics of multilingual misinformation through an analysis of 264,487 fact-checks spanning 95 languages. To study the evolution of claims over time and mutations across languages, we represent fact-checks with multilingual sentence embeddings and build a graph where semantically similar claims are linked. We provide quantitative evidence of repeated fact-checking efforts and establish that claims diffuse across languages. Specifically, we find that while the majority of misinformation claims are only fact-checked once, 10.26\%, corresponding to more than 27,000 claims, are checked multiple times. Using fact-checks as a proxy for the spread of misinformation, we find 32.26\% of repeated claims cross linguistic boundaries, suggesting that some misinformation permeates language barriers. However, spreading patterns exhibit strong assortativity, with misinformation more likely to spread within the same language or language family. Next we show that fact-checkers take more time to fact-check claims that have crossed language barriers and model the temporal and cross-lingual evolution of claims. We analyze connected components and shortest paths connecting different versions of a claim finding that claims gradually drift over time and undergo greater alteration when traversing languages. Misinformation changes over time, reducing the effectiveness of static claim matching algorithms. The findings advocate for expanded information sharing between fact-checkers globally while underscoring the importance of localized verification.},
	language = {en},
	number = {1},
	urldate = {2025-03-29},
	journal = {EPJ Data Science},
	author = {Quelle, Dorian and Cheng, Calvin Yixiang and Bovet, Alexandre and Hale, Scott A.},
	month = mar,
	year = {2025},
	keywords = {computational social science, disinformation},
	pages = {22},
}

Abstract Misinformation and disinformation are growing threats in the digital age, affecting people across languages and borders. However, no research has investigated the prevalence of multilingual misinformation and quantified the extent to which misinformation diffuses across languages. This paper investigates the prevalence and dynamics of multilingual misinformation through an analysis of 264,487 fact-checks spanning 95 languages. To study the evolution of claims over time and mutations across languages, we represent fact-checks with multilingual sentence embeddings and build a graph where semantically similar claims are linked. We provide quantitative evidence of repeated fact-checking efforts and establish that claims diffuse across languages. Specifically, we find that while the majority of misinformation claims are only fact-checked once, 10.26%, corresponding to more than 27,000 claims, are checked multiple times. Using fact-checks as a proxy for the spread of misinformation, we find 32.26% of repeated claims cross linguistic boundaries, suggesting that some misinformation permeates language barriers. However, spreading patterns exhibit strong assortativity, with misinformation more likely to spread within the same language or language family. Next we show that fact-checkers take more time to fact-check claims that have crossed language barriers and model the temporal and cross-lingual evolution of claims. We analyze connected components and shortest paths connecting different versions of a claim finding that claims gradually drift over time and undergo greater alteration when traversing languages. Misinformation changes over time, reducing the effectiveness of static claim matching algorithms. The findings advocate for expanded information sharing between fact-checkers globally while underscoring the importance of localized verification.

@inproceedings{hofererEffectiveEphemeralPropaganda2025,
	address = {Yokohama Japan},
	title = {Effective {Yet} {Ephemeral} {Propaganda} {Defense}: {There} {Needs} to {Be} {More} than {One}-{Shot} {Inoculation} to {Enhance} {Critical} {Thinking}},
	copyright = {All rights reserved},
	isbn = {979-8-4007-1395-8},
	shorttitle = {Effective {Yet} {Ephemeral} {Propaganda} {Defense}},
	url = {https://dl.acm.org/doi/10.1145/3706599.3720125},
	doi = {10.1145/3706599.3720125},
	language = {en},
	urldate = {2025-08-28},
	booktitle = {Proceedings of the {Extended} {Abstracts} of the {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {ACM},
	author = {Hoferer, Nicolas and Sprenkamp, Kilian and Quelle, Dorian Christoph and Jones, Daniel Gordon and Katashinskaya, Zoya and Bovet, Alexandre and Zavolokina, Liudmila},
	month = apr,
	year = {2025},
	note = {Citations: 1 (Crossref) [2026-03-06]
Citations: 2 (SemanticScholar) [2026-03-06]},
	keywords = {computational social science, disinformation},
	pages = {1--13},
}

@misc{koovelyEvolutionConditionalEntropy2025,
	title = {Evolution of {Conditional} {Entropy} for {Diffusion} {Dynamics} on {Graphs}},
	url = {http://arxiv.org/abs/2510.19441},
	doi = {10.48550/arXiv.2510.19441},
	abstract = {The modeling of diffusion processes on graphs is the basis for many network science and machine learning approaches. Entropic measures of network-based diffusion have recently been employed to investigate the reversibility of these processes and the diversity of the modeled systems. While results about their steady state are well-known, very few exact results about their time evolution exist. Here, we introduce the conditional entropy of heat diffusion in graphs. We demonstrate that this entropic measure satisfies the first and second laws of thermodynamics, thereby providing a physical interpretation of diffusion dynamics on networks. We outline a mathematical framework that contextualizes diffusion and conditional entropy within the theories of continuous-time Markov chains and information theory. Furthermore, we obtain explicit results for its evolution on complete, path, and circulant graphs, as well as a mean-field approximation for Erdös-Rényi graphs. We also obtain asymptotic results for general networks. Finally, we experimentally demonstrate several properties of conditional entropy for diffusion over random graphs, such as the Watts-Strogatz model.},
	urldate = {2026-03-17},
	publisher = {arXiv},
	author = {Koovely, Samuel and Bovet, Alexandre},
	month = oct,
	year = {2025},
	note = {arXiv:2510.19441 [math]},
	keywords = {diffusion, network science},
}

The modeling of diffusion processes on graphs is the basis for many network science and machine learning approaches. Entropic measures of network-based diffusion have recently been employed to investigate the reversibility of these processes and the diversity of the modeled systems. While results about their steady state are well-known, very few exact results about their time evolution exist. Here, we introduce the conditional entropy of heat diffusion in graphs. We demonstrate that this entropic measure satisfies the first and second laws of thermodynamics, thereby providing a physical interpretation of diffusion dynamics on networks. We outline a mathematical framework that contextualizes diffusion and conditional entropy within the theories of continuous-time Markov chains and information theory. Furthermore, we obtain explicit results for its evolution on complete, path, and circulant graphs, as well as a mean-field approximation for Erdös-Rényi graphs. We also obtain asymptotic results for general networks. Finally, we experimentally demonstrate several properties of conditional entropy for diffusion over random graphs, such as the Watts-Strogatz model.

@misc{quelleSimpleContagionDrives2025,
	title = {Simple contagion drives population-scale platform migration},
	copyright = {arXiv.org perpetual, non-exclusive license},
	url = {https://arxiv.org/abs/2505.24801},
	doi = {10.48550/ARXIV.2505.24801},
	abstract = {Social media platforms mediate professional communication, political expression, and community formation, making the rare instances when users collectively abandon an incumbent platform particularly consequential. Strong network effects raise switching costs and strengthen incumbents' positions, making coordinated exit difficult. Here we link 276,431 scholars on Twitter/X to their respective new profiles among the universe of all 16.7 million Bluesky accounts, tracked from January 2023 to December 2024, using a scalable, high-precision cross-platform matching pipeline. Exploiting exogenous variation from Brazil's court-ordered suspension of Twitter/X and a dynamic matching design, we show that adoption is peer-driven, treatment effects are short-lived and dose-dependent, and contagion is simple, not complex. Three patterns characterize adoption and retention. Adoption concentrates among users deeply embedded in Twitter's social graph. Public political expression predicts migration, consistent with homophilous inflows into a largely left-of-center Bluesky information space. Early reconnection with prior contacts predicts longer tenure and engagement. Our findings provide the first population-scale causal evidence of peer influence in a social media platform migration by exploiting exogenous exposure variation in a natural experiment and using daily dynamic matching. Rather than the complex contagion mechanism often emphasized in the literature, contagion is predominantly simple. Our findings recast migration as a multi-homing strategy that insures against governance uncertainty and show that users who quickly reconnect with prior contacts remain active longer on Bluesky.},
	urldate = {2026-03-13},
	publisher = {arXiv},
	author = {Quelle, Dorian and Denker, Frederic and Garg, Prashant and Bovet, Alexandre},
	year = {2025},
	note = {arXiv:22505.24801},
	keywords = {computational social science, network science, social media},
}

Social media platforms mediate professional communication, political expression, and community formation, making the rare instances when users collectively abandon an incumbent platform particularly consequential. Strong network effects raise switching costs and strengthen incumbents' positions, making coordinated exit difficult. Here we link 276,431 scholars on Twitter/X to their respective new profiles among the universe of all 16.7 million Bluesky accounts, tracked from January 2023 to December 2024, using a scalable, high-precision cross-platform matching pipeline. Exploiting exogenous variation from Brazil's court-ordered suspension of Twitter/X and a dynamic matching design, we show that adoption is peer-driven, treatment effects are short-lived and dose-dependent, and contagion is simple, not complex. Three patterns characterize adoption and retention. Adoption concentrates among users deeply embedded in Twitter's social graph. Public political expression predicts migration, consistent with homophilous inflows into a largely left-of-center Bluesky information space. Early reconnection with prior contacts predicts longer tenure and engagement. Our findings provide the first population-scale causal evidence of peer influence in a social media platform migration by exploiting exogenous exposure variation in a natural experiment and using daily dynamic matching. Rather than the complex contagion mechanism often emphasized in the literature, contagion is predominantly simple. Our findings recast migration as a multi-homing strategy that insures against governance uncertainty and show that users who quickly reconnect with prior contacts remain active longer on Bluesky.

@inproceedings{zhangQuantifyingSpreadOnline2025,
	title = {Quantifying the {Spread} of {Online} {Incivility} in {Brazilian} {Politics}},
	volume = {19},
	copyright = {All rights reserved},
	issn = {2334-0770, 2162-3449},
	url = {https://ojs.aaai.org/index.php/ICWSM/article/view/35931},
	doi = {10.1609/icwsm.v19i1.35931},
	abstract = {Incivility refers to behaviors that violate collective norms and disrupt cooperation within the political process. Although large-scale online data and automated techniques have enabled the quantitative analysis of uncivil discourse, prior research has predominantly focused on impoliteness or toxicity, often overlooking other behaviors that undermine democratic values. To address this gap, we propose a multidimensional conceptual framework encompassing Impoliteness (IMP), Physical Harm and Violent Political Rhetoric (PHAVPR), Hate Speech and Stereotyping (HSST), and Threats to Democratic Institutions and Values (THREAT). Using this framework, we measure the spread of online political incivility in Brazil using approximately 5 million tweets posted by 2,307 political influencers during the 2022 Brazilian general election. Through statistical modeling and network analysis, we examine the dynamics of uncivil posts at different election stages, identify key disseminators and audiences, and explore the mechanisms driving the spread of uncivil information online. Our findings indicate that impoliteness is more likely to surge during election campaigns. In contrast, the other dimensions of incivility are often triggered by specific violent events. Moreover, we find that left-aligned individual influencers are the primary disseminators of online incivility in the Brazilian Twitter/X sphere and that they disseminate not only direct incivility but also indirect incivility when discussing or opposing incivility expressed by others. They relay those content from politicians, media agents, and individuals to reach broader audiences, revealing a diffusion pattern mixing the direct and two-step flows of communication theory. This study offers new insights into the multidimensional nature of incivility in Brazilian politics and provides a conceptual framework that can be extended to other political contexts.},
	urldate = {2025-09-15},
	booktitle = {Proceedings of the {International} {AAAI} {Conference} on {Web} and {Social} {Media}},
	author = {Zhang, Yuan and Amsler, Michael and Herrero, Laia Castro and Esser, Frank and Bovet, Alexandre},
	month = jun,
	year = {2025},
	keywords = {computational social science, network science, social media},
	pages = {2241--2259},
}

Incivility refers to behaviors that violate collective norms and disrupt cooperation within the political process. Although large-scale online data and automated techniques have enabled the quantitative analysis of uncivil discourse, prior research has predominantly focused on impoliteness or toxicity, often overlooking other behaviors that undermine democratic values. To address this gap, we propose a multidimensional conceptual framework encompassing Impoliteness (IMP), Physical Harm and Violent Political Rhetoric (PHAVPR), Hate Speech and Stereotyping (HSST), and Threats to Democratic Institutions and Values (THREAT). Using this framework, we measure the spread of online political incivility in Brazil using approximately 5 million tweets posted by 2,307 political influencers during the 2022 Brazilian general election. Through statistical modeling and network analysis, we examine the dynamics of uncivil posts at different election stages, identify key disseminators and audiences, and explore the mechanisms driving the spread of uncivil information online. Our findings indicate that impoliteness is more likely to surge during election campaigns. In contrast, the other dimensions of incivility are often triggered by specific violent events. Moreover, we find that left-aligned individual influencers are the primary disseminators of online incivility in the Brazilian Twitter/X sphere and that they disseminate not only direct incivility but also indirect incivility when discussing or opposing incivility expressed by others. They relay those content from politicians, media agents, and individuals to reach broader audiences, revealing a diffusion pattern mixing the direct and two-step flows of communication theory. This study offers new insights into the multidimensional nature of incivility in Brazilian politics and provides a conceptual framework that can be extended to other political contexts.

@article{brabantLongitudinalModularityModularity2025,
	title = {Longitudinal modularity, a modularity for link streams},
	volume = {14},
	copyright = {© The Author(s) 2025},
	issn = {2193-1127},
	url = {https://epjds.epj.org/articles/epjdata/abs/2025/01/13688_2025_Article_529/13688_2025_Article_529.html},
	doi = {10.1140/epjds/s13688-025-00529-x},
	abstract = {Temporal networks are commonly used to model real-life phenomena. When these phenomena represent interactions and are captured at a fine-grained temporal resolution, they are modeled as link streams. Community detection is an essential network analysis task. Although many methods exist for static networks, and some methods have been developed for temporal networks represented as sequences of snapshots, few works can handle directly link streams. This article introduces the first adaptation of the well-known Modularity quality function to link streams. Unlike existing methods, it is independent of the time scale of analysis. After introducing the quality function, and its relation to existing static and dynamic definitions of Modularity, we show experimentally its relevance for dynamic community evaluation.},
	language = {en},
	number = {1},
	urldate = {2026-03-13},
	journal = {EPJ Data Science},
	publisher = {Springer Berlin Heidelberg},
	author = {Brabant, Victor and Asgari, Yasaman and Borgnat, Pierre and Bonifati, Angela and Cazabet, Rémy},
	month = dec,
	year = {2025},
	keywords = {community detection, network science, temporal networks},
	pages = {12},
}

Temporal networks are commonly used to model real-life phenomena. When these phenomena represent interactions and are captured at a fine-grained temporal resolution, they are modeled as link streams. Community detection is an essential network analysis task. Although many methods exist for static networks, and some methods have been developed for temporal networks represented as sequences of snapshots, few works can handle directly link streams. This article introduces the first adaptation of the well-known Modularity quality function to link streams. Unlike existing methods, it is independent of the time scale of analysis. After introducing the quality function, and its relation to existing static and dynamic definitions of Modularity, we show experimentally its relevance for dynamic community evaluation.

@article{pedreschiCommunityDetectionDirected2025,
	title = {Community detection on directed networks with missing edges},
	volume = {6},
	copyright = {All rights reserved},
	issn = {2632-072X},
	url = {https://iopscience.iop.org/article/10.1088/2632-072X/ade5e4},
	doi = {10.1088/2632-072X/ade5e4},
	abstract = {Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can be estimated. In this work, we extend the recently developed Flow Stability framework, originally designed for detecting communities in time-varying networks, to address the problem of community detection in weighted, directed networks with missing links. Our approach leverages known uncertainty levels in nodes’ out-degrees to enhance the robustness of community detection. Through comparisons on synthetic networks and a real-world network of messaging channels on the Telegram platform, we demonstrate that our method delivers more reliable community structures, even when a significant portion of data is missing.},
	language = {en},
	number = {2},
	urldate = {2025-10-08},
	journal = {Journal of Physics: Complexity},
	author = {Pedreschi, Nicola and Lambiotte, Renaud and Bovet, Alexandre},
	month = jun,
	year = {2025},
	keywords = {community detection, network science},
	pages = {025018},
}

Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can be estimated. In this work, we extend the recently developed Flow Stability framework, originally designed for detecting communities in time-varying networks, to address the problem of community detection in weighted, directed networks with missing links. Our approach leverages known uncertainty levels in nodes’ out-degrees to enhance the robustness of community detection. Through comparisons on synthetic networks and a real-world network of messaging channels on the Telegram platform, we demonstrate that our method delivers more reliable community structures, even when a significant portion of data is missing.

@article{rehmannGraphSpringNeural2025,
	title = {Graph spring neural {ODEs} for link sign prediction},
	volume = {114},
	copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License},
	issn = {0885-6125, 1573-0565},
	url = {https://link.springer.com/10.1007/s10994-025-06794-1},
	doi = {10.1007/s10994-025-06794-1},
	abstract = {Signed graphs allow for encoding positive and negative relations between nodes and are used to model various online activities. Node representation learning for signed graphs is a well-studied task with important applications such as sign prediction. While the size of datasets is ever-increasing, recent methods often sacrifice scalability for accuracy. We propose a novel message-passing layer architecture called graph spring network (GSN) modeled after spring forces. We combine it with a graph neural ordinary differential equations (ODEs) formalism to optimize the system dynamics in embedding space to solve a downstream prediction task. Once the dynamics is learned, embedding generation for novel datasets is done by solving the ODEs in time using a numerical integration scheme. Our GSN layer leverages the fast-to-compute edge vector directions and learnable scalar functions that only depend on nodes’ distances in latent space to compute the nodes’ positions. Conversely, graph convolution and graph attention network layers rely on learnable vector functions that require the full positions of input nodes in latent space. We propose a specific implementation called spring-neural-network using a set of small neural networks mimicking attracting and repulsing spring forces that we train for link sign prediction. Experiments show that our method achieves accuracy close to the state-of-the-art methods with node generation time speedups factor of up to 28,000 on large graphs.},
	language = {en},
	number = {7},
	urldate = {2025-10-08},
	journal = {Machine Learning},
	author = {Rehmann, Andrin and Bovet, Alexandre},
	month = jul,
	year = {2025},
	keywords = {network science},
	pages = {152},
}

Signed graphs allow for encoding positive and negative relations between nodes and are used to model various online activities. Node representation learning for signed graphs is a well-studied task with important applications such as sign prediction. While the size of datasets is ever-increasing, recent methods often sacrifice scalability for accuracy. We propose a novel message-passing layer architecture called graph spring network (GSN) modeled after spring forces. We combine it with a graph neural ordinary differential equations (ODEs) formalism to optimize the system dynamics in embedding space to solve a downstream prediction task. Once the dynamics is learned, embedding generation for novel datasets is done by solving the ODEs in time using a numerical integration scheme. Our GSN layer leverages the fast-to-compute edge vector directions and learnable scalar functions that only depend on nodes’ distances in latent space to compute the nodes’ positions. Conversely, graph convolution and graph attention network layers rely on learnable vector functions that require the full positions of input nodes in latent space. We propose a specific implementation called spring-neural-network using a set of small neural networks mimicking attracting and repulsing spring forces that we train for link sign prediction. Experiments show that our method achieves accuracy close to the state-of-the-art methods with node generation time speedups factor of up to 28,000 on large graphs.

@article{quellePerilsPromisesFactchecking2024,
	title = {The perils and promises of fact-checking with large language models},
	volume = {7},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2624-8212},
	url = {https://www.frontiersin.org/articles/10.3389/frai.2024.1341697/full},
	doi = {10.3389/frai.2024.1341697},
	abstract = {Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large language models (LLMs) like GPT-4 are increasingly trusted to write academic papers, lawsuits, and news articles and to verify information, emphasizing their role in discerning truth from falsehood and the importance of being able to verify their outputs. Understanding the capacities and limitations of LLMs in fact-checking tasks is therefore essential for ensuring the health of our information ecosystem. Here, we evaluate the use of LLM agents in fact-checking by having them phrase queries, retrieve contextual data, and make decisions. Importantly, in our framework, agents explain their reasoning and cite the relevant sources from the retrieved context. Our results show the enhanced prowess of LLMs when equipped with contextual information. GPT-4 outperforms GPT-3, but accuracy varies based on query language and claim veracity. While LLMs show promise in fact-checking, caution is essential due to inconsistent accuracy. Our investigation calls for further research, fostering a deeper comprehension of when agents succeed and when they fail.},
	urldate = {2024-09-17},
	journal = {Frontiers in Artificial Intelligence},
	publisher = {Frontiers Media SA},
	author = {Quelle, Dorian and Bovet, Alexandre},
	month = feb,
	year = {2024},
	keywords = {computational social science, disinformation},
}

Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large language models (LLMs) like GPT-4 are increasingly trusted to write academic papers, lawsuits, and news articles and to verify information, emphasizing their role in discerning truth from falsehood and the importance of being able to verify their outputs. Understanding the capacities and limitations of LLMs in fact-checking tasks is therefore essential for ensuring the health of our information ecosystem. Here, we evaluate the use of LLM agents in fact-checking by having them phrase queries, retrieve contextual data, and make decisions. Importantly, in our framework, agents explain their reasoning and cite the relevant sources from the retrieved context. Our results show the enhanced prowess of LLMs when equipped with contextual information. GPT-4 outperforms GPT-3, but accuracy varies based on query language and claim veracity. While LLMs show promise in fact-checking, caution is essential due to inconsistent accuracy. Our investigation calls for further research, fostering a deeper comprehension of when agents succeed and when they fail.

@article{serafinoSuspendedAccountsAlign2024,
	title = {Suspended accounts align with the {Internet} {Research} {Agency} misinformation campaign to influence the 2016 {US} election},
	volume = {13},
	copyright = {https://creativecommons.org/licenses/by/4.0},
	issn = {2193-1127},
	url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-024-00464-3},
	doi = {10.1140/epjds/s13688-024-00464-3},
	abstract = {The ongoing debate surrounding the impact of the Internet Research Agency’s (IRA) social media campaign during the 2016 U.S. presidential election has largely overshadowed the involvement of other actors. Our analysis brings to light a substantial group of suspended Twitter users, outnumbering the IRA user group by a factor of 60, who align with the ideologies of the IRA campaign. Our study demonstrates that this group of suspended Twitter accounts significantly influenced individuals categorized as undecided or weak supporters, potentially with the aim of swaying their opinions, as indicated by Granger causality.},
	language = {en},
	number = {1},
	urldate = {2024-09-17},
	journal = {EPJ Data Science},
	publisher = {Springer Science and Business Media LLC},
	author = {Serafino, Matteo and Zhou, Zhenkun and Andrade, José S. and Bovet, Alexandre and Makse, Hernán A.},
	month = apr,
	year = {2024},
	keywords = {computational social science, disinformation, network science, social media},
}

The ongoing debate surrounding the impact of the Internet Research Agency’s (IRA) social media campaign during the 2016 U.S. presidential election has largely overshadowed the involvement of other actors. Our analysis brings to light a substantial group of suspended Twitter users, outnumbering the IRA user group by a factor of 60, who align with the ideologies of the IRA campaign. Our study demonstrates that this group of suspended Twitter accounts significantly influenced individuals categorized as undecided or weak supporters, potentially with the aim of swaying their opinions, as indicated by Granger causality.

@article{bradshawStrategicStorytellingRussian2024,
	title = {Strategic {Storytelling}: {Russian} {State}-{Backed} {Media} {Coverage} of the {Ukraine} {War}},
	volume = {36},
	issn = {1471-6909},
	shorttitle = {Strategic {Storytelling}},
	url = {https://doi.org/10.1093/ijpor/edae028},
	doi = {10.1093/ijpor/edae028},
	abstract = {Strategic narratives help shape wars. In modern history, strategic narratives were used during the Cold War, the War in Afghanistan, and the Iraq War to justify military invasions and manipulate opponents. During the 2022 Russian invasion of Ukraine, Russia was accused of weaponizing its state-backed media outlets to promote a pro-Russian version of the war. Consequently, Russian state-backed media faced a series of new sanctions from Western governments and technology companies. While some studies have sought to identify disinformation about the war, less research has focused on understanding how these stories come together as narratives, particularly in non-English language contexts. Grounded in strategic narrative theory, we analyze Russian state-backed media coverage of the Ukraine war across 12 languages. Using topic modeling and narrative analysis, we find that Russian state-backed media focused primarily on promoting identity narratives, forming an image that Russia is powerful, Ukraine is evil, and the West is hypocritical. Russian strategic narratives both converged and diverged across languages and outlets in ways that met Russia’s desired image and objectives in each region. This paper allows us to better theorize the evolving and transformative role of strategic narrative in Russian state-backed news media during times of conflict.},
	number = {3},
	urldate = {2026-03-13},
	journal = {International Journal of Public Opinion Research},
	author = {Bradshaw, Samantha and Elswah, Mona and Haque, Monzima and Quelle, Dorian},
	month = sep,
	year = {2024},
	keywords = {computational social science, disinformation},
	pages = {edae028},
}

Strategic narratives help shape wars. In modern history, strategic narratives were used during the Cold War, the War in Afghanistan, and the Iraq War to justify military invasions and manipulate opponents. During the 2022 Russian invasion of Ukraine, Russia was accused of weaponizing its state-backed media outlets to promote a pro-Russian version of the war. Consequently, Russian state-backed media faced a series of new sanctions from Western governments and technology companies. While some studies have sought to identify disinformation about the war, less research has focused on understanding how these stories come together as narratives, particularly in non-English language contexts. Grounded in strategic narrative theory, we analyze Russian state-backed media coverage of the Ukraine war across 12 languages. Using topic modeling and narrative analysis, we find that Russian state-backed media focused primarily on promoting identity narratives, forming an image that Russia is powerful, Ukraine is evil, and the West is hypocritical. Russian strategic narratives both converged and diverged across languages and outlets in ways that met Russia’s desired image and objectives in each region. This paper allows us to better theorize the evolving and transformative role of strategic narrative in Russian state-backed news media during times of conflict.

@inproceedings{asgariMosaicBenchmarkNetworks2024,
	address = {Cham},
	title = {Mosaic {Benchmark} {Networks}: {Modular} {Link} {Streams} for {Testing} {Dynamic} {Community} {Detection} {Algorithms}},
	isbn = {978-3-031-53499-7},
	shorttitle = {Mosaic {Benchmark} {Networks}},
	doi = {10.1007/978-3-031-53499-7_17},
	abstract = {Community structure is a critical feature of real networks, providing insights into nodes’ internal organization. Nowadays, with the availability of highly detailed temporal networks such as link streams, studying community structures becomes more complex due to increased data precision and time sensitivity. Despite numerous algorithms developed in the past decade for dynamic community discovery, assessing their performance on link streams remains a challenge. Synthetic benchmark graphs are a well-accepted approach for evaluating static community detection algorithms. Additionally, there have been some proposals for slowly evolving communities in low-resolution temporal networks like snapshots. Nevertheless, this approach is not yet suitable for link streams. To bridge this gap, we introduce a novel framework that generates synthetic modular link streams with predefined communities. Subsequently, we evaluate established dynamic community detection methods to uncover limitations that may not be evident in snapshots with slowly evolving communities. While no method emerges as a clear winner, we observe notable differences among them.},
	language = {en},
	booktitle = {Complex {Networks} \& {Their} {Applications} {XII}},
	publisher = {Springer Nature Switzerland},
	author = {Asgari, Yasaman and Cazabet, Remy and Borgnat, Pierre},
	editor = {Cherifi, Hocine and Rocha, Luis M. and Cherifi, Chantal and Donduran, Murat},
	year = {2024},
	keywords = {community detection, network science, temporal networks},
	pages = {209--222},
}

Community structure is a critical feature of real networks, providing insights into nodes’ internal organization. Nowadays, with the availability of highly detailed temporal networks such as link streams, studying community structures becomes more complex due to increased data precision and time sensitivity. Despite numerous algorithms developed in the past decade for dynamic community discovery, assessing their performance on link streams remains a challenge. Synthetic benchmark graphs are a well-accepted approach for evaluating static community detection algorithms. Additionally, there have been some proposals for slowly evolving communities in low-resolution temporal networks like snapshots. Nevertheless, this approach is not yet suitable for link streams. To bridge this gap, we introduce a novel framework that generates synthetic modular link streams with predefined communities. Subsequently, we evaluate established dynamic community detection methods to uncover limitations that may not be evident in snapshots with slowly evolving communities. While no method emerges as a clear winner, we observe notable differences among them.

@article{flaminoPoliticalPolarizationNews2023,
	title = {Political polarization of news media and influencers on {Twitter} in the 2016 and 2020 {US} presidential elections},
	volume = {7},
	copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},
	issn = {2397-3374},
	url = {https://www.nature.com/articles/s41562-023-01550-8},
	doi = {10.1038/s41562-023-01550-8},
	abstract = {Abstract
            Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.},
	language = {en},
	number = {6},
	urldate = {2023-10-22},
	journal = {Nature Human Behaviour},
	author = {Flamino, James and Galeazzi, Alessandro and Feldman, Stuart and Macy, Michael W. and Cross, Brendan and Zhou, Zhenkun and Serafino, Matteo and Bovet, Alexandre and Makse, Hernán A. and Szymanski, Boleslaw K.},
	month = mar,
	year = {2023},
	keywords = {computational social science, disinformation, network science, polarization, social media},
	pages = {904--916},
}

Abstract Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.

@inproceedings{bovetEvolvingLiaisonsTransaction2023,
	address = {Sendai, Japan},
	title = {The {Evolving} {Liaisons} between the {Transaction} {Networks} of {Bitcoin} and {Its} {Price} {Dynamics}},
	copyright = {Creative Commons Attribution 4.0 International License (CC-BY)},
	isbn = {978-4-89027-153-5},
	url = {https://journals.jps.jp/doi/10.7566/JPSCP.40.011002},
	doi = {10.7566/JPSCP.40.011002},
	abstract = {Cryptocurrencies (the most paradigmatic blockchain-based systems) are distributed systems that allow to exchange tokens among participants. These cryptocurrencies can also be acquired in exchange markets. The availability of the historical bookkeeping of cryptocurrency transfers in a public ledger opens up the possibility of understanding the relationship between aggregate users’ behaviour and the cryptocurrency pricing in exchange markets. This paper analyses the properties of the transaction network of Bitcoin. We consider different representations over a period of nine years since its creation and involving 16 million users and 283 million transactions. Importantly, these transactions do not include orders filled in exchange markets, which are settled outside of the blockchain, and ultimately determine Bitcoin price. By analysing these networks, we show the existence of Granger causal relationships between Bitcoin price movements and changes of its transaction network topology. Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of users’ activity.},
	language = {en},
	urldate = {2023-10-02},
	booktitle = {Proceedings of {Blockchain} {Kaigi} 2022 ({BCK22})},
	publisher = {Journal of the Physical Society of Japan},
	author = {Bovet, Alexandre and Campajola, Carlo and Mottes, Francesco and Restocchi, Valerio and Vallarano, Nicolò and Squartini, Tiziano and Tessone, Claudio J.},
	month = sep,
	year = {2023},
	keywords = {network science},
}

Cryptocurrencies (the most paradigmatic blockchain-based systems) are distributed systems that allow to exchange tokens among participants. These cryptocurrencies can also be acquired in exchange markets. The availability of the historical bookkeeping of cryptocurrency transfers in a public ledger opens up the possibility of understanding the relationship between aggregate users’ behaviour and the cryptocurrency pricing in exchange markets. This paper analyses the properties of the transaction network of Bitcoin. We consider different representations over a period of nine years since its creation and involving 16 million users and 283 million transactions. Importantly, these transactions do not include orders filled in exchange markets, which are settled outside of the blockchain, and ultimately determine Bitcoin price. By analysing these networks, we show the existence of Granger causal relationships between Bitcoin price movements and changes of its transaction network topology. Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of users’ activity.

@article{bovetFlowStabilityDynamic2022,
	title = {Flow stability for dynamic community detection},
	volume = {8},
	copyright = {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA)},
	issn = {2375-2548},
	url = {https://www.science.org/doi/10.1126/sciadv.abj3063},
	doi = {10.1126/sciadv.abj3063},
	abstract = {Many systems exhibit complex temporal dynamics due to the presence of different processes taking place simultaneously. An important task in these systems is to extract a simplified view of their time-dependent network of interactions. Community detection in temporal networks usually relies on aggregation over time windows or consider sequences of different stationary epochs. For dynamics-based methods, attempts to generalize static-network methodologies also face the fundamental difficulty that a stationary state of the dynamics does not always exist. Here, we derive a method based on a dynamical process evolving on the temporal network. Our method allows dynamics that do not reach a steady state and uncovers two sets of communities for a given time interval that accounts for the ordering of edges in forward and backward time. We show that our method provides a natural way to disentangle the different dynamical scales present in a system with synthetic and real-world examples.
          , 
            The flow stability method extracts simplified descriptions of complex time-resolved datasets at different dynamical scales.},
	language = {en},
	number = {19},
	urldate = {2022-05-12},
	journal = {Science Advances},
	author = {Bovet, Alexandre and Delvenne, Jean-Charles and Lambiotte, Renaud},
	month = may,
	year = {2022},
	keywords = {community detection, diffusion, network science, temporal networks},
	pages = {eabj3063},
}

Many systems exhibit complex temporal dynamics due to the presence of different processes taking place simultaneously. An important task in these systems is to extract a simplified view of their time-dependent network of interactions. Community detection in temporal networks usually relies on aggregation over time windows or consider sequences of different stationary epochs. For dynamics-based methods, attempts to generalize static-network methodologies also face the fundamental difficulty that a stationary state of the dynamics does not always exist. Here, we derive a method based on a dynamical process evolving on the temporal network. Our method allows dynamics that do not reach a steady state and uncovers two sets of communities for a given time interval that accounts for the ordering of edges in forward and backward time. We show that our method provides a natural way to disentangle the different dynamical scales present in a system with synthetic and real-world examples. , The flow stability method extracts simplified descriptions of complex time-resolved datasets at different dynamical scales.

@article{bovetOrganizationEvolutionUK2022,
	title = {Organization and evolution of the {UK} far-right network on {Telegram}},
	volume = {7},
	issn = {2364-8228},
	url = {https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00513-8},
	doi = {10.1007/s41109-022-00513-8},
	abstract = {The instant messaging platform Telegram has become popular among the far-right movements in the US and UK in recent years. These groups use public Telegram channels and group chats to disseminate hate speech, disinformation, and conspiracy theories. Recent works revealed that the far-right Telegram network structure is decentralized and formed of several communities divided mostly along ideological and national lines. Here, we investigated the UK far-right network on Telegram and are interested in understanding the different roles of different channels and their influence relations. We apply a community detection method, based on the clustering of a flow of random walkers, that allows us to uncover the organization of the Telegram network in communities with different roles. We find three types of communities: (1) upstream communities contain mostly group chats that comment on content from channels in the rest of the network; (2) core communities contain broadcast channels tightly connected to each other and can be seen as forming echo chambers; (3) downstream communities contain popular channels that are highly referenced by other channels. We find that the network is composed of two main sub-networks: one containing mainly channels related to the English-speaking far-right movements and one with channels in Russian. We analyze the dynamics of the different communities and the most shared external links in the different types of communities over a period going from 2015 to 2020. We find that different types of communities have different dynamics and share links to different types of websites. We finish by discussing several directions for further work.},
	language = {en},
	number = {1},
	urldate = {2022-11-24},
	journal = {Applied Network Science},
	author = {Bovet, Alexandre and Grindrod, Peter},
	month = nov,
	year = {2022},
	keywords = {computational social science, diffusion, network science, social media},
	pages = {76},
}

The instant messaging platform Telegram has become popular among the far-right movements in the US and UK in recent years. These groups use public Telegram channels and group chats to disseminate hate speech, disinformation, and conspiracy theories. Recent works revealed that the far-right Telegram network structure is decentralized and formed of several communities divided mostly along ideological and national lines. Here, we investigated the UK far-right network on Telegram and are interested in understanding the different roles of different channels and their influence relations. We apply a community detection method, based on the clustering of a flow of random walkers, that allows us to uncover the organization of the Telegram network in communities with different roles. We find three types of communities: (1) upstream communities contain mostly group chats that comment on content from channels in the rest of the network; (2) core communities contain broadcast channels tightly connected to each other and can be seen as forming echo chambers; (3) downstream communities contain popular channels that are highly referenced by other channels. We find that the network is composed of two main sub-networks: one containing mainly channels related to the English-speaking far-right movements and one with channels in Russian. We analyze the dynamics of the different communities and the most shared external links in the different types of communities over a period going from 2015 to 2020. We find that different types of communities have different dynamics and share links to different types of websites. We finish by discussing several directions for further work.

@incollection{Bovet2020,
	address = {Berlin, Heidelberg},
	title = {Centralities in {Complex} {Networks}},
	copyright = {All rights reserved},
	isbn = {978-3-642-27737-5},
	url = {https://doi.org/10.1007/978-3-642-27737-5_765-1},
	doi = {10.1007/978-3-642-27737-5_765-1},
	abstract = {In network science complex systems are represented as a mathematical graphs consisting of a set of nodes representing the components and a set of edges representing their interactions. The framework of networks has led to significant advances in the understanding of the structure, formation and function of complex systems. Social and biological processes such as the dynamics of epidemics, the diffusion of information in social media, the interactions between species in ecosystems or the communication between neurons in our brains are all actively studied using dynamical models on complex networks. In all of these systems, the patterns of connections at the individual level play a fundamental role on the global dynamics and finding the most important nodes allows one to better understand and predict their behaviors. An important research effort in network science has therefore been dedicated to the development of methods allowing to find the most important nodes in networks. In this short entry, we describe network centrality measures based on the notions of network traversal they rely on. This entry aims at being an introduction to this extremely vast topic, with many contributions from several fields, and is by no means an exhaustive review of all the literature about network centralities.},
	booktitle = {Encyclopedia of {Complexity} and {Systems} {Science}},
	publisher = {Springer Berlin Heidelberg},
	author = {Bovet, Alexandre and Makse, Hernán A.},
	editor = {Meyers, Robert A},
	year = {2021},
	note = {arXiv: 2105.01931},
	keywords = {network science},
	pages = {1--11},
}

In network science complex systems are represented as a mathematical graphs consisting of a set of nodes representing the components and a set of edges representing their interactions. The framework of networks has led to significant advances in the understanding of the structure, formation and function of complex systems. Social and biological processes such as the dynamics of epidemics, the diffusion of information in social media, the interactions between species in ecosystems or the communication between neurons in our brains are all actively studied using dynamical models on complex networks. In all of these systems, the patterns of connections at the individual level play a fundamental role on the global dynamics and finding the most important nodes allows one to better understand and predict their behaviors. An important research effort in network science has therefore been dedicated to the development of methods allowing to find the most important nodes in networks. In this short entry, we describe network centrality measures based on the notions of network traversal they rely on. This entry aims at being an introduction to this extremely vast topic, with many contributions from several fields, and is by no means an exhaustive review of all the literature about network centralities.

@inproceedings{Gutierrez-Gomez2020,
	title = {Multi-{Scale} {Anomaly} {Detection} on {Attributed} {Networks}},
	volume = {34},
	issn = {2374-3468},
	url = {https://aaai.org/ojs/index.php/AAAI/article/view/5409},
	doi = {10.1609/aaai.v34i01.5409},
	abstract = {Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes. Finding anomalies in these systems is crucial for detecting abuses such as credit card frauds, web spams or network intrusions. Intuitively, anomalous nodes are defined as nodes whose attributes differ starkly from the attributes of a certain set of nodes of reference, called the context of the anomaly. While some methods have proposed to spot anomalies locally, globally or within a community context, the problem remain challenging due to the multi-scale composition of real networks and the heterogeneity of node metadata. Here, we propose a principled way to uncover outlier nodes simultaneously with the context with respect to which they are anomalous, at all relevant scales of the network. We characterize anomalous nodes in terms of the concentration retained for each node after smoothing specific signals localized on the vertices of the graph. Besides, we introduce a graph signal processing formulation of the Markov stability framework used in community detection, in order to find the context of anomalies. The performance of our method is assessed on synthetic and real-world attributed networks and shows superior results concerning state of the art algorithms. Finally, we show the scalability of our approach in large networks employing Chebychev polynomial approximations.},
	number = {01},
	booktitle = {Proceedings of the 34th {AAAI} {Conference} on {Artificial} {Intelligence}},
	publisher = {AAAI Press},
	author = {Gutiérrez-Gómez, Leonardo and Bovet, Alexandre and Delvenne, Jean-Charles},
	month = apr,
	year = {2020},
	note = {Place: Palo Alto, California USA},
	keywords = {diffusion, network science},
	pages = {678--685},
}

Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes. Finding anomalies in these systems is crucial for detecting abuses such as credit card frauds, web spams or network intrusions. Intuitively, anomalous nodes are defined as nodes whose attributes differ starkly from the attributes of a certain set of nodes of reference, called the context of the anomaly. While some methods have proposed to spot anomalies locally, globally or within a community context, the problem remain challenging due to the multi-scale composition of real networks and the heterogeneity of node metadata. Here, we propose a principled way to uncover outlier nodes simultaneously with the context with respect to which they are anomalous, at all relevant scales of the network. We characterize anomalous nodes in terms of the concentration retained for each node after smoothing specific signals localized on the vertices of the graph. Besides, we introduce a graph signal processing formulation of the Markov stability framework used in community detection, in order to find the context of anomalies. The performance of our method is assessed on synthetic and real-world attributed networks and shows superior results concerning state of the art algorithms. Finally, we show the scalability of our approach in large networks employing Chebychev polynomial approximations.

@article{Bovet2019,
	title = {Influence of fake news in {Twitter} during the 2016 {US} presidential election},
	volume = {10},
	issn = {2041-1723},
	url = {http://arxiv.org/abs/1803.08491},
	doi = {10.1038/s41467-018-07761-2},
	abstract = {The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources. co,we find that 25\% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.},
	number = {1},
	journal = {Nature Communications},
	publisher = {Springer US},
	author = {Bovet, Alexandre and Makse, Hernán A.},
	month = jan,
	year = {2019},
	keywords = {computational social science, diffusion, disinformation, network science, social media},
	pages = {7},
}

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources. co,we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.

@article{Bovet2018,
	title = {Validation of {Twitter} opinion trends with national polling aggregates: {Hillary} {Clinton} vs {Donald} {Trump}},
	volume = {8},
	issn = {2045-2322},
	url = {http://arxiv.org/abs/1610.01587},
	doi = {10.1038/s41598-018-26951-y},
	abstract = {Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls.},
	number = {1},
	journal = {Scientific Reports},
	author = {Bovet, Alexandre and Morone, Flaviano and Makse, Hernán A.},
	month = dec,
	year = {2018},
	note = {arXiv: 1610.01587},
	keywords = {computational social science, network science, social media},
	pages = {8673},
}

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls.

@article{Furno2016,
	title = {Non-diffusive transport of suprathermal ions by intermittent turbulent structures},
	volume = {58},
	issn = {0741-3335},
	url = {http://stacks.iop.org/0741-3335/58/i=1/a=014023?key=crossref.1f402a37d87fd89ab485e359be191467},
	doi = {10.1088/0741-3335/58/1/014023},
	number = {1},
	journal = {Plasma Physics and Controlled Fusion},
	publisher = {IOP Publishing},
	author = {Furno, I and Bovet, A and Fasoli, A and Gauthey, C and Gustafson, K and Ricci, P and van Milligen, B Ph},
	month = jan,
	year = {2016},
	keywords = {diffusion, plasma physics},
	pages = {014023},
}

@article{Bovet2015arxiv,
	title = {An {Introduction} to {Non}-diffusive {Transport} {Models}},
	url = {http://arxiv.org/abs/1508.01879},
	abstract = {The process of diffusion is the most elementary stochastic transport process. Brownian motion, the representative model of diffusion, played a important role in the advancement of scientific fields such as physics, chemistry, biology and finance. However, in recent decades, non-diffusive transport processes with non-Brownian statistics were observed experimentally in a multitude of scientific fields. Examples include human travel, in-cell dynamics, the motion of bright points on the solar surface, the transport of charge carriers in amorphous semiconductors, the propagation of contaminants in groundwater, the search patterns of foraging animals and the transport of energetic particles in turbulent plasmas. These examples showed that the assumptions of the classical diffusion paradigm, assuming an underlying uncorrelated (Markovian), Gaussian stochastic process, need to be relaxed to describe transport processes exhibiting a non-local character and exhibiting long-range correlations. This article does not aim at presenting a complete review of non-diffusive transport, but rather an introduction for readers not familiar with the topic. For more in depth reviews, we recommend some references in the following. First, we recall the basics of the classical diffusion model and then we present two approaches of possible generalizations of this model: the Continuous-Time-Random-Walk (CTRW) and the fractional L{\textbackslash}'evy motion (fLm).},
	journal = {ArXiv e-prints [cond-mat.stat-mech]},
	author = {Bovet, Alexandre},
	year = {2015},
	note = {arXiv: 1508.01879},
	keywords = {diffusion, plasma physics},
}

The process of diffusion is the most elementary stochastic transport process. Brownian motion, the representative model of diffusion, played a important role in the advancement of scientific fields such as physics, chemistry, biology and finance. However, in recent decades, non-diffusive transport processes with non-Brownian statistics were observed experimentally in a multitude of scientific fields. Examples include human travel, in-cell dynamics, the motion of bright points on the solar surface, the transport of charge carriers in amorphous semiconductors, the propagation of contaminants in groundwater, the search patterns of foraging animals and the transport of energetic particles in turbulent plasmas. These examples showed that the assumptions of the classical diffusion paradigm, assuming an underlying uncorrelated (Markovian), Gaussian stochastic process, need to be relaxed to describe transport processes exhibiting a non-local character and exhibiting long-range correlations. This article does not aim at presenting a complete review of non-diffusive transport, but rather an introduction for readers not familiar with the topic. For more in depth reviews, we recommend some references in the following. First, we recall the basics of the classical diffusion model and then we present two approaches of possible generalizations of this model: the Continuous-Time-Random-Walk (CTRW) and the fractional L\'evy motion (fLm).

@article{Bovet2015PRE,
	title = {Nondiffusive transport regimes for suprathermal ions in turbulent plasmas},
	volume = {91},
	issn = {1539-3755},
	url = {http://link.aps.org/doi/10.1103/PhysRevE.91.041101},
	doi = {10.1103/PhysRevE.91.041101},
	number = {4},
	journal = {Physical Review E},
	author = {Bovet, A. and Fasoli, A. and Ricci, P. and Furno, I. and Gustafson, K.},
	month = apr,
	year = {2015},
	keywords = {diffusion, plasma physics},
	pages = {041101},
}

@article{Zimbardo2015,
	title = {Superdiffusive transport in laboratory and astrophysical plasmas},
	volume = {81},
	issn = {0022-3778},
	url = {http://www.journals.cambridge.org/abstract_S0022377815001117},
	doi = {10.1017/S0022377815001117},
	abstract = {In the last few years it has been demonstrated, both by data analysis and by numerical simulations, that the transport of energetic particles in the presence of magnetic turbulence can be superdiffusive rather than normal diffusive (Gaussian). The term ‘superdiffusive’ refers to the mean square displacement of particle positions growing superlinearly with time, as compared to the normal linear growth. The so-called anomalous transport, which in general comprises both subdiffusion and superdiffusion, has gained growing attention during the last two decades in many fields including laboratory plasma physics, and recently in astrophysics and space physics. Here we show a number of examples, both from laboratory and from astrophysical plasmas, where superdiffusive transport has been identified, with a focus on what could be the main influence of superdiffusion on fundamental processes like diffusive shock acceleration and heliospheric energetic particle propagation. For laboratory plasmas, superdiffusion appears to be due to the presence of electrostatic turbulence which creates long-range correlations and convoluted structures in perpendicular transport: this corresponds to a similar phenomenon in the propagation of solar energetic particles (SEPs) which leads to SEP dropouts. For the propagation of energetic particles accelerated at interplanetary shocks in the solar wind, parallel superdiffusion seems to be prevailing; this is based on a pitch-angle scattering process different from that envisaged by quasi-linear theory, and this emphasizes the importance of nonlinear interactions and trapping effects. In the case of supernova remnant shocks, parallel superdiffusion is possible at quasi-parallel shocks, as occurring in the interplanetary space, and perpendicular superdiffusion is possible at quasi-perpendicular shocks, as corresponding to Richardson diffusion: therefore, cosmic ray acceleration at supernova remnant shocks should be formulated in terms of superdiffusion. The possible relations among anomalous transport in laboratory, heliospheric, and astrophysical plasmas will be indicated.},
	number = {06},
	journal = {Journal of Plasma Physics},
	author = {Zimbardo, G. and Amato, E. and Bovet, A. and Effenberger, F. and Fasoli, A. and Fichtner, H. and Furno, I. and Gustafson, K. and Ricci, P. and Perri, S.},
	month = dec,
	year = {2015},
	note = {ISBN: 0022377815001},
	keywords = {diffusion, plasma physics},
	pages = {495810601},
}

In the last few years it has been demonstrated, both by data analysis and by numerical simulations, that the transport of energetic particles in the presence of magnetic turbulence can be superdiffusive rather than normal diffusive (Gaussian). The term ‘superdiffusive’ refers to the mean square displacement of particle positions growing superlinearly with time, as compared to the normal linear growth. The so-called anomalous transport, which in general comprises both subdiffusion and superdiffusion, has gained growing attention during the last two decades in many fields including laboratory plasma physics, and recently in astrophysics and space physics. Here we show a number of examples, both from laboratory and from astrophysical plasmas, where superdiffusive transport has been identified, with a focus on what could be the main influence of superdiffusion on fundamental processes like diffusive shock acceleration and heliospheric energetic particle propagation. For laboratory plasmas, superdiffusion appears to be due to the presence of electrostatic turbulence which creates long-range correlations and convoluted structures in perpendicular transport: this corresponds to a similar phenomenon in the propagation of solar energetic particles (SEPs) which leads to SEP dropouts. For the propagation of energetic particles accelerated at interplanetary shocks in the solar wind, parallel superdiffusion seems to be prevailing; this is based on a pitch-angle scattering process different from that envisaged by quasi-linear theory, and this emphasizes the importance of nonlinear interactions and trapping effects. In the case of supernova remnant shocks, parallel superdiffusion is possible at quasi-parallel shocks, as occurring in the interplanetary space, and perpendicular superdiffusion is possible at quasi-perpendicular shocks, as corresponding to Richardson diffusion: therefore, cosmic ray acceleration at supernova remnant shocks should be formulated in terms of superdiffusion. The possible relations among anomalous transport in laboratory, heliospheric, and astrophysical plasmas will be indicated.

@article{Furno2015a,
	title = {Plasma turbulence, suprathermal ion dynamics and code validation on the basic plasma physics device {TORPEX}},
	volume = {81},
	issn = {0022-3778},
	url = {http://www.journals.cambridge.org/abstract_S0022377815000161},
	doi = {10.1017/S0022377815000161},
	abstract = {The TORPEX basic plasma physics device at the Center for Plasma Physics Research (CRPP) in Lausanne, Switzerland is described. In TORPEX, simple magnetized toroidal configurations, a paradigm for the tokamak scrape-off layer (SOL), as well as more complex magnetic geometries of direct relevance for fusion are produced. Plasmas of different gases are created and sustained by microwaves in the electron-cyclotron (EC) frequency range. Full diagnostic access allows for a complete characterization of plasma fluctuations and wave fields throughout the entire plasma volume, opening new avenues to validate numerical codes. We detail recent advances in the understanding of basic aspects of plasma turbulence, including its development from linearly unstable electrostatic modes, the formation of filamentary structures, or blobs, and its influence on the transport of energy, plasma bulk and suprathermal ions. We present a methodology for the validation of plasma turbulence codes, which focuses on quantitative assessment of the agreement between numerical simulations and TORPEX experimental data.},
	number = {03},
	journal = {Journal of Plasma Physics},
	author = {Furno, I. and Avino, F. and Bovet, A. and Diallo, A. and Fasoli, A. and Gustafson, K. and Iraji, D. and Labit, B. and Loizu, J. and Müller, S. H. and Plyushchev, G. and Podestà, M. and Poli, F. M. and Ricci, P. and Theiler, C.},
	month = jun,
	year = {2015},
	keywords = {plasma physics},
	pages = {345810301},
}

The TORPEX basic plasma physics device at the Center for Plasma Physics Research (CRPP) in Lausanne, Switzerland is described. In TORPEX, simple magnetized toroidal configurations, a paradigm for the tokamak scrape-off layer (SOL), as well as more complex magnetic geometries of direct relevance for fusion are produced. Plasmas of different gases are created and sustained by microwaves in the electron-cyclotron (EC) frequency range. Full diagnostic access allows for a complete characterization of plasma fluctuations and wave fields throughout the entire plasma volume, opening new avenues to validate numerical codes. We detail recent advances in the understanding of basic aspects of plasma turbulence, including its development from linearly unstable electrostatic modes, the formation of filamentary structures, or blobs, and its influence on the transport of energy, plasma bulk and suprathermal ions. We present a methodology for the validation of plasma turbulence codes, which focuses on quantitative assessment of the agreement between numerical simulations and TORPEX experimental data.

@article{Bovet2014NF,
	title = {Transport equation describing fractional {Lévy} motion of suprathermal ions in {TORPEX}},
	volume = {54},
	issn = {0029-5515},
	url = {http://stacks.iop.org/0029-5515/54/i=10/a=104009?key=crossref.2e17c909257be18296d22e371341a68d},
	doi = {10.1088/0029-5515/54/10/104009},
	number = {10},
	urldate = {2014-11-20},
	journal = {Nuclear Fusion},
	author = {Bovet, A. and Gamarino, M. and Furno, I. and Ricci, P. and Fasoli, A. and Gustafson, K. and Newman, D.E. and Sánchez, R.},
	month = oct,
	year = {2014},
	keywords = {diffusion, plasma physics},
	pages = {104009},
}

@article{Bovet2014PRL,
	title = {Time-{Resolved} {Measurements} of {Suprathermal} {Ion} {Transport} {Induced} by {Intermittent} {Plasma} {Blob} {Filaments}},
	volume = {113},
	issn = {0031-9007},
	url = {http://link.aps.org/doi/10.1103/PhysRevLett.113.225001},
	doi = {10.1103/PhysRevLett.113.225001},
	abstract = {Suprathermal ion turbulent transport in magnetized plasmas is generally nondiffusive, ranging from subdiffusive to superdiffusive depending on the interplay of the turbulent structures and the suprathermal ion orbits. Here, we present time-resolved measurements of the cross-field suprathermal ion transport in a toroidal magnetized turbulent plasma. Measurements in the superdiffusive regime are characterized by a higher intermittency than in the subdiffusive regime. Using conditional averaging, we show that, when the transport is superdiffusive, suprathermal ions are transported by intermittent field-elongated turbulent structures that are radially propagating},
	number = {22},
	urldate = {2014-11-27},
	journal = {Physical Review Letters},
	publisher = {American Physical Society},
	author = {Bovet, A and Fasoli, A and Furno, I},
	month = nov,
	year = {2014},
	keywords = {diffusion, plasma physics},
	pages = {225001},
}

Suprathermal ion turbulent transport in magnetized plasmas is generally nondiffusive, ranging from subdiffusive to superdiffusive depending on the interplay of the turbulent structures and the suprathermal ion orbits. Here, we present time-resolved measurements of the cross-field suprathermal ion transport in a toroidal magnetized turbulent plasma. Measurements in the superdiffusive regime are characterized by a higher intermittency than in the subdiffusive regime. Using conditional averaging, we show that, when the transport is superdiffusive, suprathermal ions are transported by intermittent field-elongated turbulent structures that are radially propagating

@article{Perrone2013,
	title = {Nonclassical {Transport} and {Particle}-{Field} {Coupling}: from {Laboratory} {Plasmas} to the {Solar} {Wind}},
	volume = {178},
	issn = {0038-6308},
	url = {http://link.springer.com/10.1007/s11214-013-9966-9},
	doi = {10.1007/s11214-013-9966-9},
	number = {2-4},
	urldate = {2013-11-07},
	journal = {Space Science Reviews},
	author = {Perrone, D. and Dendy, R. O. and Furno, I. and Sánchez, R. and Zimbardo, G. and Bovet, A. and Fasoli, A. and Gustafson, K. and Perri, S. and Ricci, P. and Valentini, F.},
	month = mar,
	year = {2013},
	keywords = {diffusion, plasma physics},
	pages = {233--270},
}

@article{0741-3335-55-12-124021,
	title = {Three-dimensional measurements of non-diffusive fast ion transport in {TORPEX}},
	volume = {55},
	issn = {0741-3335},
	url = {http://stacks.iop.org/0741-3335/55/i=12/a=124021},
	doi = {10.1088/0741-3335/55/12/124021},
	abstract = {In the basic plasma physics device TORPEX, progress in the fundamental understanding of supra-thermal ion transport is achieved by extensive sets of three-dimensional (3D) data, together with numerical simulations of supra-thermal ion tracers in fluid turbulent fields. A miniaturized lithium 6+ ion source injects fast ions with energies up to 1 KeV and a double-gridded energy analyzer is used as a detector. The source is mounted on a toroidally movable system and the detector can be moved in the poloidal cross-section, allowing one to reconstruct 3D fast ion current profiles. Synchronous detection is used to enhance the signal-to-noise ratio. A modulated biasing voltage is applied to the fast ion source and an analog lock-in amplifier is used to demodulate the detector signal. The analog lock-in amplifier is specially designed to remove the capacitive noise associated with the voltage modulation. Radial transport of the fast ions, associated with plasma turbulence, is characterized. A synthetic diagnostic allows comparing the experimental results with numerical simulations of the fast ion transport in a global fluid simulation of the TORPEX plasma. A good agreement is shown.},
	number = {12},
	urldate = {2013-11-29},
	journal = {Plasma Physics and Controlled Fusion},
	author = {Bovet, A and Furno, I and Fasoli, A and Gustafson, K and Ricci, P},
	month = dec,
	year = {2013},
	keywords = {diffusion, plasma physics},
	pages = {124021},
}

In the basic plasma physics device TORPEX, progress in the fundamental understanding of supra-thermal ion transport is achieved by extensive sets of three-dimensional (3D) data, together with numerical simulations of supra-thermal ion tracers in fluid turbulent fields. A miniaturized lithium 6+ ion source injects fast ions with energies up to 1 KeV and a double-gridded energy analyzer is used as a detector. The source is mounted on a toroidally movable system and the detector can be moved in the poloidal cross-section, allowing one to reconstruct 3D fast ion current profiles. Synchronous detection is used to enhance the signal-to-noise ratio. A modulated biasing voltage is applied to the fast ion source and an analog lock-in amplifier is used to demodulate the detector signal. The analog lock-in amplifier is specially designed to remove the capacitive noise associated with the voltage modulation. Radial transport of the fast ions, associated with plasma turbulence, is characterized. A synthetic diagnostic allows comparing the experimental results with numerical simulations of the fast ion transport in a global fluid simulation of the TORPEX plasma. A good agreement is shown.

@article{Fasoli2013,
	title = {Basic investigations of electrostatic turbulence and its interaction with plasma and suprathermal ions in a simple magnetized toroidal plasma},
	volume = {53},
	issn = {0029-5515},
	url = {http://stacks.iop.org/0029-5515/53/i=6/a=063013?key=crossref.9d1f4cf2ccf1fec61dc3e83c50faf34e},
	doi = {10.1088/0029-5515/53/6/063013},
	number = {6},
	urldate = {2013-11-21},
	journal = {Nuclear Fusion},
	author = {Fasoli, A. and Avino, F. and Bovet, A. and Furno, I. and Gustafson, K. and Jolliet, S. and Loizu, J. and Malinverni, D. and Ricci, P. and Riva, F. and Theiler, C. and Spolaore, M. and Vianello, N.},
	month = jun,
	year = {2013},
	keywords = {plasma physics},
	pages = {063013},
}

@article{Bovet2012,
	title = {Investigation of fast ion transport in {TORPEX}},
	volume = {52},
	issn = {0029-5515},
	url = {http://iopscience.iop.org/0029-5515/52/9/094017/},
	doi = {10.1088/0029-5515/52/9/094017},
	number = {9},
	urldate = {2013-02-28},
	journal = {Nuclear Fusion},
	author = {Bovet, Alexandre and Furno, Ivo and Fasoli, Ambrogio and Gustafson, Kyle and Ricci, Paolo},
	month = sep,
	year = {2012},
	keywords = {diffusion, plasma physics},
	pages = {094017},
}

@article{Gustafson2012,
	title = {Suprathermal ion transport in simple magnetized torus configurations},
	volume = {19},
	issn = {1070664X},
	url = {http://link.aip.org/link/PHPAEN/v19/i6/p062306/s1&Agg=doi},
	doi = {10.1063/1.4725420},
	number = {6},
	urldate = {2012-06-21},
	journal = {Physics of Plasmas},
	author = {Gustafson, K. and Ricci, P. and Bovet, A. and Furno, I. and Fasoli, A.},
	year = {2012},
	keywords = {diffusion, plasma physics},
	pages = {062306},
}

@article{Heidbrink2012,
	title = {Measurements of interactions between waves and energetic ions in basic plasma experiments},
	volume = {54},
	issn = {0741-3335},
	url = {http://stacks.iop.org/0741-3335/54/i=12/a=124007?key=crossref.ea283c943fcbb833b94885e571598c3f},
	doi = {10.1088/0741-3335/54/12/124007},
	number = {12},
	urldate = {2013-03-05},
	journal = {Plasma Physics and Controlled Fusion},
	author = {Heidbrink, W W and Boehmer, H and McWilliams, R and Preiwisch, A and Zhang, Y and Zhao, L and Zhou, S and Bovet, A and Fasoli, A and Furno, I and Gustafson, K and Ricci, P and Carter, T and Leneman, D and Tripathi, S K P and Vincena, S},
	month = dec,
	year = {2012},
	keywords = {plasma physics},
	pages = {124007},
}