Longitudinal modularity, a modularity for link streams. Brabant, V., Asgari, Y., Borgnat, P., Bonifati, A., & Cazabet, R. EPJ Data Science, 14(1):12, Springer Berlin Heidelberg, December, 2025. ![Longitudinal modularity, a modularity for link streams [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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{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},
}
Downloads: 0
{"_id":"97DuPNbcyCs5exzCZ","bibbaseid":"brabant-asgari-borgnat-bonifati-cazabet-longitudinalmodularityamodularityforlinkstreams-2025","author_short":["Brabant, V.","Asgari, Y.","Borgnat, P.","Bonifati, A.","Cazabet, R."],"bibdata":{"bibtype":"article","type":"article","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":[{"propositions":[],"lastnames":["Brabant"],"firstnames":["Victor"],"suffixes":[]},{"propositions":[],"lastnames":["Asgari"],"firstnames":["Yasaman"],"suffixes":[]},{"propositions":[],"lastnames":["Borgnat"],"firstnames":["Pierre"],"suffixes":[]},{"propositions":[],"lastnames":["Bonifati"],"firstnames":["Angela"],"suffixes":[]},{"propositions":[],"lastnames":["Cazabet"],"firstnames":["Rémy"],"suffixes":[]}],"month":"December","year":"2025","keywords":"community detection, network science, temporal networks","pages":"12","bibtex":"@article{brabantLongitudinalModularityModularity2025,\n\ttitle = {Longitudinal modularity, a modularity for link streams},\n\tvolume = {14},\n\tcopyright = {© The Author(s) 2025},\n\tissn = {2193-1127},\n\turl = {https://epjds.epj.org/articles/epjdata/abs/2025/01/13688_2025_Article_529/13688_2025_Article_529.html},\n\tdoi = {10.1140/epjds/s13688-025-00529-x},\n\tabstract = {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.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2026-03-13},\n\tjournal = {EPJ Data Science},\n\tpublisher = {Springer Berlin Heidelberg},\n\tauthor = {Brabant, Victor and Asgari, Yasaman and Borgnat, Pierre and Bonifati, Angela and Cazabet, Rémy},\n\tmonth = dec,\n\tyear = {2025},\n\tkeywords = {community detection, network science, temporal networks},\n\tpages = {12},\n}\n\n\n\n","author_short":["Brabant, V.","Asgari, Y.","Borgnat, P.","Bonifati, A.","Cazabet, R."],"key":"brabantLongitudinalModularityModularity2025","id":"brabantLongitudinalModularityModularity2025","bibbaseid":"brabant-asgari-borgnat-bonifati-cazabet-longitudinalmodularityamodularityforlinkstreams-2025","role":"author","urls":{"Paper":"https://epjds.epj.org/articles/epjdata/abs/2025/01/13688_2025_Article_529/13688_2025_Article_529.html"},"keyword":["community detection","network science","temporal networks"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero-group/abovet/6456604","dataSources":["taMT8ogTGkddhSYpK","GTtGZ5jx4FWS8myri","5fCpyQ7SJbQa9pasN"],"keywords":["community detection","network science","temporal networks"],"search_terms":["longitudinal","modularity","modularity","link","streams","brabant","asgari","borgnat","bonifati","cazabet"],"title":"Longitudinal modularity, a modularity for link streams","year":2025}