var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://drive.google.com/uc?export=download&id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF\",\"jsonp\":\"1\",\"authorFirst\":\"1\",\"sort\":\"-year\",\"theme\":\"side\",\"filter\":\"keywords:network science\\\\b\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Aref\"],\"firstnames\":[\"Samin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mason\"],\"firstnames\":[\"Andrew\",\"J\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Mark\",\"C.\"],\"suffixes\":[]}],\"title\":\"A modeling and computational study of the frustration index in signed networks\",\"number\":\"1\",\"pages\":\"95-110\",\"volume\":\"75\",\"abstract\":\"Computing the frustration index of a signed graph is a key step toward solving problems in many fields including social networks, political science, physics, chemistry, and biology. The frustration index determines the distance of a network from a state of total structural balance. Although the definition of the frustration index goes back to the 1950's, its exact algorithmic computation, which is closely related to classic NP-hard graph problems, has only become a focus in recent years. We develop three new binary linear programming models to compute the frustration index exactly and efficiently as the solution to a global optimisation problem. Solving the models with prioritised branching and valid inequalities in Gurobi, we can compute the frustration index of real signed networks with over 15000 edges in less than a minute on inexpensive hardware. We provide extensive performance analysis for both random and real signed networks and show that our models outperform all existing approaches by large factors. Based on solve time, algorithm output, and effective branching factor we highlight the superiority of our models to both exact and heuristic methods in the literature.\",\"journal\":\"Networks\",\"keywords\":\"network science, computation, graphs\",\"publisher\":\"Wiley\",\"url_paper\":\"https://arxiv.org/abs/1611.09030\",\"year\":\"2020\",\"bibtex\":\"@Article{aref2020modeling,\\n  author    = {Aref, Samin and Mason, Andrew J and Wilson, Mark C.},\\n  title     = {A modeling and computational study of the frustration index in signed networks},\\n  number    = {1},\\n  pages     = {95-110},\\n  volume    = {75},\\n  abstract  = {Computing the frustration index of a signed graph is a key step toward\\nsolving problems in many fields including social networks, political\\nscience, physics, chemistry, and biology. The frustration index\\ndetermines the distance of a network from a state of total structural\\nbalance. Although the definition of the frustration index goes back to\\nthe 1950's, its exact algorithmic computation, which is closely related\\nto classic NP-hard graph problems, has only become a focus in recent\\nyears. We develop three new binary linear programming models to compute\\nthe frustration index exactly and efficiently as the solution to a\\nglobal optimisation problem. Solving the models with prioritised\\nbranching and valid inequalities in Gurobi, we can compute the\\nfrustration index of real signed networks with over 15000 edges in less\\nthan a minute on inexpensive hardware. We provide extensive performance\\nanalysis for both random and real signed networks and show that our\\nmodels outperform all existing approaches by large factors. Based on\\nsolve time, algorithm output, and effective branching factor we\\nhighlight the superiority of our models to both exact and heuristic\\nmethods in the literature.},\\n  journal   = {Networks},\\n  keywords  = {network science, computation, graphs},\\n  publisher = {Wiley},\\n  url_paper = {https://arxiv.org/abs/1611.09030},\\n  year      = {2020},\\n}\\n\\n\",\"author_short\":[\"Aref, S.\",\"Mason, A. J\",\"Wilson, M. C.\"],\"key\":\"aref2020modeling\",\"id\":\"aref2020modeling\",\"bibbaseid\":\"aref-mason-wilson-amodelingandcomputationalstudyofthefrustrationindexinsignednetworks-2020\",\"role\":\"author\",\"urls\":{\" paper\":\"https://arxiv.org/abs/1611.09030\"},\"keyword\":[\"network science\",\"computation\",\"graphs\"],\"metadata\":{\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Information extraction framework to build legislation network\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sakhaee\"],\"firstnames\":[\"Neda\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Mark\",\"C.\"],\"suffixes\":[]}],\"journal\":\"Artificial Intelligence and Law\",\"pages\":\"1-24\",\"year\":\"2020\",\"publisher\":\"Springer Netherlands\",\"keywords\":\"network science\",\"url_paper\":\"https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf\",\"abstract\":\"This paper concerns an Information Extraction process for building a dynamic Legislation Network from legal documents. Unlike supervised learning approaches which require additional calculations, the idea here is to apply Information Extraction methodologies by identifying distinct expressions in legal text and extract quality network information. The study highlights the importance of data accuracy in network analysis and improves approximate string matching techniques for producing reliable network datasets with more than 98 percent precision and recall. The values, applications, and the complexity of the created dynamic Legislation Network are also discussed and challenged.\",\"bibtex\":\"@article{sakhaee2020information,\\n  title={Information extraction framework to build legislation network},\\n  author={Sakhaee, Neda and Wilson, Mark C.},\\n  journal={Artificial Intelligence and Law},\\n  pages={1-24},\\n  year={2020},\\n  publisher={Springer Netherlands},\\n  keywords={network science},\\n  url_Paper={https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf},\\n  abstract={This paper concerns an Information Extraction process for building a\\ndynamic Legislation Network from legal documents. Unlike supervised\\nlearning approaches which require additional calculations, the idea here\\nis to apply Information Extraction methodologies by identifying distinct\\nexpressions in legal text and extract quality network information. The\\nstudy highlights the importance of data accuracy in network analysis and\\nimproves approximate string matching techniques for producing reliable\\nnetwork datasets with more than 98 percent precision and recall. The\\nvalues, applications, and the complexity of the created dynamic\\nLegislation Network are also discussed and challenged.}\\n}\\n\\n\",\"author_short\":[\"Sakhaee, N.\",\"Wilson, M. C.\"],\"key\":\"sakhaee2020information\",\"id\":\"sakhaee2020information\",\"bibbaseid\":\"sakhaee-wilson-informationextractionframeworktobuildlegislationnetwork-2020\",\"role\":\"author\",\"urls\":{\" paper\":\"https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf\"},\"keyword\":[\"network science\"],\"metadata\":{\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Balance and frustration in signed networks\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Aref\"],\"firstnames\":[\"Samin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Mark\",\"C.\"],\"suffixes\":[]}],\"journal\":\"Journal of Complex Networks\",\"volume\":\"7\",\"number\":\"2\",\"pages\":\"163-189\",\"year\":\"2019\",\"publisher\":\"Oxford University Press\",\"keywords\":\"network science, graphs\",\"url_paper\":\"https://academic.oup.com/comnet/article-abstract/7/2/163/5074195\",\"abstract\":\"The frustration index is a key measure for analysing signed networks, which has been underused due to its computational complexity. We use an exact optimisation-based method to analyse frustration as a global structural property of signed networks coming from diverse application areas. In the classic friend-enemy interpretation of balance theory, a by-product of computing the frustration index is the partitioning of nodes into two internally solidary but mutually hostile groups. The main purpose of this paper is to present general methodology for answering questions related to partial balance in signed networks, and apply it to a range of representative examples that are now analysable because of advances in computational methods. We provide exact numerical results on social and biological signed networks, networks of formal alliances and antagonisms between countries, and financial portfolio networks. Molecular graphs of carbon and Ising models are also considered. We point out several mistakes in the signed networks literature caused by inaccurate computation, implementation errors or inappropriate measures.\",\"bibtex\":\"@article{aref2019balance,\\n  title={Balance and frustration in signed networks},\\n  author={Aref, Samin and Wilson, Mark C.},\\n  journal={Journal of Complex Networks},\\n  volume={7},\\n  number={2},\\n  pages={163-189},\\n  year={2019},\\n  publisher={Oxford University Press},\\n  keywords={network science, graphs},\\n  url_Paper= {https://academic.oup.com/comnet/article-abstract/7/2/163/5074195},\\n  abstract={The frustration index is a key measure for analysing signed networks,\\nwhich has been underused due to its computational complexity. We use an\\nexact optimisation-based method to analyse frustration as a global\\nstructural property of signed networks coming from diverse application\\nareas. In the classic friend-enemy interpretation of balance theory, a\\nby-product of computing the frustration index is the partitioning of\\nnodes into two internally solidary but mutually hostile groups. The main\\npurpose of this paper is to present general methodology for answering\\nquestions related to partial balance in signed networks, and apply it to\\na range of representative examples that are now analysable because of\\nadvances in computational methods. We provide exact numerical results on\\nsocial and biological signed networks, networks of formal alliances and\\nantagonisms between countries, and financial portfolio networks.\\nMolecular graphs of carbon and Ising models are also considered. We\\npoint out several mistakes in the signed networks literature caused by\\ninaccurate computation, implementation errors or inappropriate\\nmeasures.}\\n}\\n\\n\",\"author_short\":[\"Aref, S.\",\"Wilson, M. C.\"],\"key\":\"aref2019balance\",\"id\":\"aref2019balance\",\"bibbaseid\":\"aref-wilson-balanceandfrustrationinsignednetworks-2019\",\"role\":\"author\",\"urls\":{\" paper\":\"https://academic.oup.com/comnet/article-abstract/7/2/163/5074195\"},\"keyword\":[\"network science\",\"graphs\"],\"metadata\":{\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Computing the line index of balance using integer programming optimisation\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Aref\"],\"firstnames\":[\"Samin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mason\"],\"firstnames\":[\"Andrew\",\"J\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Mark\",\"C.\"],\"suffixes\":[]}],\"booktitle\":\"Optimization Problems in Graph Theory\",\"pages\":\"65-84\",\"year\":\"2018\",\"publisher\":\"Springer, Cham\",\"keywords\":\"network science, computation, graphs\",\"url_paper\":\"https://arxiv.org/abs/1710.09876\",\"abstract\":\"An important measure of signed graphs is the line index of balance which has several applications in many fields. However, this graph-theoretic measure was underused for decades because of the inherent complexity in its computation which is closely related to solving NP-hard graph optimisation problems like MAXCUT. We develop new quadratic and linear programming models to compute the line index of balance exactly. Using the Gurobi integer programming optimisation solver, we evaluate the line index of balance on real-world and synthetic datasets. The synthetic data involves Erdős-Rényi graphs, Barabási-Albert graphs, and specially structured random graphs. We also use well known datasets from the sociology literature, such as signed graphs inferred from students' choice and rejection as well as datasets from the biology literature including gene regulatory networks. The results show that exact values of the line index of balance in relatively large signed graphs can be efficiently computed using our suggested optimisation models. We find that most real-world social networks and some biological networks have small line index of balance which indicates that they are close to balanced.\",\"bibtex\":\"@incollection{aref2018computing,\\n  title={Computing the line index of balance using integer programming optimisation},\\n  author={Aref, Samin and Mason, Andrew J and Wilson, Mark C.},\\n  booktitle={Optimization Problems in Graph Theory},\\n  pages={65-84},\\n  year={2018},\\n  publisher={Springer, Cham},\\n  keywords={network science, computation, graphs},\\n  url_Paper={https://arxiv.org/abs/1710.09876},\\n  abstract={An important measure of signed graphs is the line index of balance which\\nhas several applications in many fields. However, this graph-theoretic\\nmeasure was underused for decades because of the inherent complexity in\\nits computation which is closely related to solving NP-hard graph\\noptimisation problems like MAXCUT. We develop new quadratic and linear\\nprogramming models to compute the line index of balance exactly. Using\\nthe Gurobi integer programming optimisation solver, we evaluate the line\\nindex of balance on real-world and synthetic datasets. The synthetic\\ndata involves Erd\\\\H{o}s-R\\\\'{e}nyi graphs, Barab\\\\'{a}si-Albert graphs,\\nand specially structured random graphs. We also use well known datasets\\nfrom the sociology literature, such as signed graphs inferred from\\nstudents' choice and rejection as well as datasets from the biology\\nliterature including gene regulatory networks. The results show that\\nexact values of the line index of balance in relatively large signed\\ngraphs can be efficiently computed using our suggested optimisation\\nmodels. We find that most real-world social networks and some biological\\nnetworks have small line index of balance which indicates that they are\\nclose to balanced.}\\n}\\n\\n\",\"author_short\":[\"Aref, S.\",\"Mason, A. J\",\"Wilson, M. C.\"],\"key\":\"aref2018computing\",\"id\":\"aref2018computing\",\"bibbaseid\":\"aref-mason-wilson-computingthelineindexofbalanceusingintegerprogrammingoptimisation-2018\",\"role\":\"author\",\"urls\":{\" paper\":\"https://arxiv.org/abs/1710.09876\"},\"keyword\":[\"network science\",\"computation\",\"graphs\"],\"metadata\":{\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Measuring partial balance in signed networks\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Aref\"],\"firstnames\":[\"Samin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Mark\",\"C.\"],\"suffixes\":[]}],\"journal\":\"Journal of Complex Networks\",\"volume\":\"6\",\"number\":\"4\",\"pages\":\"566-595\",\"year\":\"2018\",\"publisher\":\"Oxford University Press\",\"keywords\":\"network science, graphs\",\"url_paper\":\"http://arxiv.org/abs/1509.04037\",\"abstract\":\"Is the enemy of an enemy necessarily a friend? If not, to what extent does this tend to hold? Such questions were formulated in terms of signed (social) networks and necessary and sufficient conditions for a network to be ‘balanced’ were obtained around 1960. Since then the idea that signed networks tend over time to become more balanced has been widely used in several application areas. However, investigation of this hypothesis has been complicated by the lack of a standard measure of partial balance, since complete balance is almost never achieved in practice. We formalize the concept of a measure of partial balance, discuss various measures, compare the measures on synthetic datasets and investigate their axiomatic properties. The synthetic data involves Erdős-Rényi and specially structured random graphs. We show that some measures behave better than others in terms of axioms and ability to differentiate between graphs. We also use well-known data sets from the sociology and biology literature, such as Read's New Guinean tribes, gene regulatory networks related to two organisms, and a network involving senate bill co-sponsorship. Our results show that substantially different levels of partial balance is observed under cycle-based, eigenvalue-based and frustration-based measures. We make some recommendations for measures to be used in future work.\",\"bibtex\":\"@article{aref2018measuring,\\n  title={Measuring partial balance in signed networks},\\n  author={Aref, Samin and Wilson, Mark C.},\\n  journal={Journal of Complex Networks},\\n  volume={6},\\n  number={4},\\n  pages={566-595},\\n  year={2018},\\n  publisher={Oxford University Press},\\n  keywords={network science, graphs},\\n  url_Paper={http://arxiv.org/abs/1509.04037},\\n  abstract={Is the enemy of an enemy necessarily a friend? If not, to what extent\\ndoes this tend to hold? Such questions were formulated in terms of\\nsigned (social) networks and necessary and sufficient conditions for a\\nnetwork to be ‘balanced’ were obtained around 1960. Since then the idea\\nthat signed networks tend over time to become more balanced has been\\nwidely used in several application areas. However, investigation of this\\nhypothesis has been complicated by the lack of a standard measure of\\npartial balance, since complete balance is almost never achieved in\\npractice. We formalize the concept of a measure of partial balance,\\ndiscuss various measures, compare the measures on synthetic datasets and\\ninvestigate their axiomatic properties. The synthetic data involves\\nErd\\\\H{o}s-R\\\\'{e}nyi and specially structured random graphs. We show that\\nsome measures behave better than others in terms of axioms and ability\\nto differentiate between graphs. We also use well-known data sets from\\nthe sociology and biology literature, such as Read's New Guinean tribes,\\ngene regulatory networks related to two organisms, and a network\\ninvolving senate bill co-sponsorship. Our results show that\\nsubstantially different levels of partial balance is observed under\\ncycle-based, eigenvalue-based and frustration-based measures. We make\\nsome recommendations for measures to be used in future work.}\\n}\\n\\n\",\"author_short\":[\"Aref, S.\",\"Wilson, M. C.\"],\"key\":\"aref2018measuring\",\"id\":\"aref2018measuring\",\"bibbaseid\":\"aref-wilson-measuringpartialbalanceinsignednetworks-2018\",\"role\":\"author\",\"urls\":{\" paper\":\"http://arxiv.org/abs/1509.04037\"},\"keyword\":[\"network science\",\"graphs\"],\"metadata\":{\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Network analysis of New Zealand legislation\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sakhaee\"],\"firstnames\":[\"Neda\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Mark\",\"C.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hendy\"],\"firstnames\":[\"Shaun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zakeri\"],\"firstnames\":[\"Golbon\"],\"suffixes\":[]}],\"journal\":\"New Zealand Law Journal\",\"pages\":\"332-337\",\"year\":\"2017\",\"publisher\":\"\",\"keywords\":\"network science\",\"url_paper\":\"\",\"abstract\":\"A summary for a legal audience of the basic early findings in Neda's PhD work about citation networks derived from legislation.\",\"bibtex\":\"@article{sakhaee2017network,\\n  title={Network analysis of New Zealand legislation},\\n  author={Sakhaee, Neda and Wilson, Mark C. and Hendy, Shaun and Zakeri, Golbon},\\n  journal={New Zealand Law Journal},\\n  pages={332-337},\\n  year={2017},\\n  publisher={},\\n  keywords={network science},\\n  url_Paper={},\\n  abstract={A summary for a legal audience of the basic early findings in Neda's PhD\\nwork about citation networks derived from legislation.}\\n}\\n\\n\",\"author_short\":[\"Sakhaee, N.\",\"Wilson, M. C.\",\"Hendy, S.\",\"Zakeri, G.\"],\"key\":\"sakhaee2017network\",\"id\":\"sakhaee2017network\",\"bibbaseid\":\"sakhaee-wilson-hendy-zakeri-networkanalysisofnewzealandlegislation-2017\",\"role\":\"author\",\"urls\":{\" paper\":\"https://drive.google.com/uc?export=download&id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF\"},\"keyword\":[\"network science\"],\"metadata\":{\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Networked crowds answer tricky questions poorly\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Girard\"],\"firstnames\":[\"Patrick\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pavlov\"],\"firstnames\":[\"Valery\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Mark\",\"C.\"],\"suffixes\":[]}],\"journal\":\"\",\"volume\":\"\",\"number\":\"\",\"pages\":\"8pp\",\"year\":\"2016\",\"publisher\":\"\",\"keywords\":\"network science\",\"url_paper\":\"\",\"abstract\":\"We focus on the following basic group decision situation, which we call iterative distributed jury (IDJ), a variant of the Delphi technique. Group members seek to answer truthfully a question having a welldefined objectively correct answer; they revise answers iteratively; only summary feedback on group members' answers is available at each iteration; individual estimates are aggregated to form a group answer. Experimental studies of the effectiveness of Delphi-like methods have yielded mixed results. To investigate further, we designed a laboratory multiple choice IDJ experiment having some novel features. One novelty was that we incentivized participants to reveal their ignorance; another is the use of both logical and factual questions. We find that, perhaps surprisingly, substantial social influence occurs even in this highly anonymized and information-restricted setting, and even for purely logical questions. Eventual group accuracy is strongly dependent on the trickiness (likelihood of being answered confidently but wrongly, a concept distinct from difficulty) of the question. Also, the bulk of learning occurs by those who were willing to admit to being undecided. We find that question factors are more important than participant characteristics. In addition to consequences for the practical use of this group decision method, our quantitative results suggest specific new models of opinion dynamics that deserve detailed study.\",\"bibtex\":\"@article{GPW2016,\\n  title={Networked crowds answer tricky questions poorly},\\n  author={Girard, Patrick and Pavlov, Valery and Wilson, Mark C.},\\n  journal={},\\n  volume={},\\n  number={},\\n  pages={8pp},\\n  year={2016},\\n  publisher={},\\n  keywords={network science},\\n  url_Paper={},\\n  abstract={We focus on the following basic group decision situation, which we call\\niterative distributed jury (IDJ), a variant of the Delphi technique.\\nGroup members seek to answer truthfully a question having a welldefined\\nobjectively correct answer; they revise answers iteratively; only\\nsummary feedback on group members' answers is available at each\\niteration; individual estimates are aggregated to form a group answer.\\nExperimental studies of the effectiveness of Delphi-like methods have\\nyielded mixed results. To investigate further, we designed a laboratory\\nmultiple choice IDJ experiment having some novel features. One novelty\\nwas that we incentivized participants to reveal their ignorance; another\\nis the use of both logical and factual questions. We find that, perhaps\\nsurprisingly, substantial social influence occurs even in this highly\\nanonymized and information-restricted setting, and even for purely\\nlogical questions. Eventual group accuracy is strongly dependent on the\\ntrickiness (likelihood of being answered confidently but wrongly, a\\nconcept distinct from difficulty) of the question. Also, the bulk of\\nlearning occurs by those who were willing to admit to being undecided.\\nWe find that question factors are more important than participant\\ncharacteristics.  In addition to consequences for the practical use of\\nthis group decision method, our quantitative results suggest specific\\nnew models of opinion dynamics that deserve detailed study.} \\n}\\n\\n\",\"author_short\":[\"Girard, P.\",\"Pavlov, V.\",\"Wilson, M. C.\"],\"key\":\"GPW2016\",\"id\":\"GPW2016\",\"bibbaseid\":\"girard-pavlov-wilson-networkedcrowdsanswertrickyquestionspoorly-2016\",\"role\":\"author\",\"urls\":{\" paper\":\"https://drive.google.com/uc?export=download&id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF\"},\"keyword\":[\"network science\"],\"metadata\":{\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"New Zealand Legislation Network.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sakhaee\"],\"firstnames\":[\"Neda\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Mark\",\"C.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zakeri\"],\"firstnames\":[\"Golbon\"],\"suffixes\":[]}],\"booktitle\":\"JURIX\",\"pages\":\"199-202\",\"year\":\"2016\",\"keywords\":\"network science\",\"url_paper\":\"SWZ2016.pdf\",\"abstract\":\"This paper concerns the recently introduced concept of Legislation Networks, with an application focus on the New Zealand legislation network. Legislation networks have some novel features which make them an excellent test case for new network science tools. They involve legal documents, but differ substantially from citation networks involving case-law, Supreme Court opinions, etc. We develop several such networks, compute relevant centrality measures, and apply community detection algorithms. We study the relationship between the legislation network measures and legal/political factors. The intention is to follow-up with more detailed studies in network science (link prediction, node attribute prediction, generative models and time evolution) and legal and political analyses.\",\"bibtex\":\"@inproceedings{sakhaee2016new,\\n  title={New Zealand Legislation Network.},\\n  author={Sakhaee, Neda and Wilson, Mark C. and Zakeri, Golbon},\\n  booktitle={JURIX},\\n  pages={199-202},\\n  year={2016},\\n  keywords={network science},\\n  url_Paper={SWZ2016.pdf},\\n  abstract={This paper concerns the recently introduced concept of Legislation\\nNetworks, with an application focus on the New Zealand legislation\\nnetwork. Legislation networks have some novel features which make them\\nan excellent test case for new network science tools. They involve legal\\ndocuments, but differ substantially from citation networks involving\\ncase-law, Supreme Court opinions, etc. We develop several such networks,\\ncompute relevant centrality measures, and apply community detection\\nalgorithms. We study the relationship between the legislation network\\nmeasures and legal/political factors. The intention is to follow-up with\\nmore detailed studies in network science (link prediction, node\\nattribute prediction, generative models and time evolution) and legal\\nand political analyses.}\\n}\\n\\n\\n\",\"author_short\":[\"Sakhaee, N.\",\"Wilson, M. C.\",\"Zakeri, G.\"],\"key\":\"sakhaee2016new\",\"id\":\"sakhaee2016new\",\"bibbaseid\":\"sakhaee-wilson-zakeri-newzealandlegislationnetwork-2016\",\"role\":\"author\",\"urls\":{\" paper\":\"https://drive.google.com/SWZ2016.pdf\"},\"keyword\":[\"network science\"],\"metadata\":{\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\"downloads\":1,\"html\":\"\"}],\n      metadata: {\"owner\":\"wilson, m\",\"authorlinks\":{\"wilson, m\":\"https://markcwilson.site/Research/networks/index.html\"}},\n      ownerID: \"X59RJvnvQGf3jqFj4\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"uc\" href=\"data:text/bibtex;base64,@Article{aref2020modeling,
  author    = {Aref, Samin and Mason, Andrew J and Wilson, Mark C.},
  title     = {A modeling and computational study of the frustration index in signed networks},
  number    = {1},
  pages     = {95-110},
  volume    = {75},
  abstract  = {Computing the frustration index of a signed graph is a key step toward
solving problems in many fields including social networks, political
science, physics, chemistry, and biology. The frustration index
determines the distance of a network from a state of total structural
balance. Although the definition of the frustration index goes back to
the 1950's, its exact algorithmic computation, which is closely related
to classic NP-hard graph problems, has only become a focus in recent
years. We develop three new binary linear programming models to compute
the frustration index exactly and efficiently as the solution to a
global optimisation problem. Solving the models with prioritised
branching and valid inequalities in Gurobi, we can compute the
frustration index of real signed networks with over 15000 edges in less
than a minute on inexpensive hardware. We provide extensive performance
analysis for both random and real signed networks and show that our
models outperform all existing approaches by large factors. Based on
solve time, algorithm output, and effective branching factor we
highlight the superiority of our models to both exact and heuristic
methods in the literature.},
  journal   = {Networks},
  keywords  = {network science, computation, graphs},
  publisher = {Wiley},
  url_paper = {https://arxiv.org/abs/1611.09030},
  year      = {2020},
}



@article{sakhaee2020information,
  title={Information extraction framework to build legislation network},
  author={Sakhaee, Neda and Wilson, Mark C.},
  journal={Artificial Intelligence and Law},
  pages={1-24},
  year={2020},
  publisher={Springer Netherlands},
  keywords={network science},
  url_Paper={https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf},
  abstract={This paper concerns an Information Extraction process for building a
dynamic Legislation Network from legal documents. Unlike supervised
learning approaches which require additional calculations, the idea here
is to apply Information Extraction methodologies by identifying distinct
expressions in legal text and extract quality network information. The
study highlights the importance of data accuracy in network analysis and
improves approximate string matching techniques for producing reliable
network datasets with more than 98 percent precision and recall. The
values, applications, and the complexity of the created dynamic
Legislation Network are also discussed and challenged.}
}



@article{aref2019balance,
  title={Balance and frustration in signed networks},
  author={Aref, Samin and Wilson, Mark C.},
  journal={Journal of Complex Networks},
  volume={7},
  number={2},
  pages={163-189},
  year={2019},
  publisher={Oxford University Press},
  keywords={network science, graphs},
  url_Paper= {https://academic.oup.com/comnet/article-abstract/7/2/163/5074195},
  abstract={The frustration index is a key measure for analysing signed networks,
which has been underused due to its computational complexity. We use an
exact optimisation-based method to analyse frustration as a global
structural property of signed networks coming from diverse application
areas. In the classic friend-enemy interpretation of balance theory, a
by-product of computing the frustration index is the partitioning of
nodes into two internally solidary but mutually hostile groups. The main
purpose of this paper is to present general methodology for answering
questions related to partial balance in signed networks, and apply it to
a range of representative examples that are now analysable because of
advances in computational methods. We provide exact numerical results on
social and biological signed networks, networks of formal alliances and
antagonisms between countries, and financial portfolio networks.
Molecular graphs of carbon and Ising models are also considered. We
point out several mistakes in the signed networks literature caused by
inaccurate computation, implementation errors or inappropriate
measures.}
}



@incollection{aref2018computing,
  title={Computing the line index of balance using integer programming optimisation},
  author={Aref, Samin and Mason, Andrew J and Wilson, Mark C.},
  booktitle={Optimization Problems in Graph Theory},
  pages={65-84},
  year={2018},
  publisher={Springer, Cham},
  keywords={network science, computation, graphs},
  url_Paper={https://arxiv.org/abs/1710.09876},
  abstract={An important measure of signed graphs is the line index of balance which
has several applications in many fields. However, this graph-theoretic
measure was underused for decades because of the inherent complexity in
its computation which is closely related to solving NP-hard graph
optimisation problems like MAXCUT. We develop new quadratic and linear
programming models to compute the line index of balance exactly. Using
the Gurobi integer programming optimisation solver, we evaluate the line
index of balance on real-world and synthetic datasets. The synthetic
data involves Erd\H{o}s-R\'{e}nyi graphs, Barab\'{a}si-Albert graphs,
and specially structured random graphs. We also use well known datasets
from the sociology literature, such as signed graphs inferred from
students' choice and rejection as well as datasets from the biology
literature including gene regulatory networks. The results show that
exact values of the line index of balance in relatively large signed
graphs can be efficiently computed using our suggested optimisation
models. We find that most real-world social networks and some biological
networks have small line index of balance which indicates that they are
close to balanced.}
}



@article{aref2018measuring,
  title={Measuring partial balance in signed networks},
  author={Aref, Samin and Wilson, Mark C.},
  journal={Journal of Complex Networks},
  volume={6},
  number={4},
  pages={566-595},
  year={2018},
  publisher={Oxford University Press},
  keywords={network science, graphs},
  url_Paper={http://arxiv.org/abs/1509.04037},
  abstract={Is the enemy of an enemy necessarily a friend? If not, to what extent
does this tend to hold? Such questions were formulated in terms of
signed (social) networks and necessary and sufficient conditions for a
network to be ‘balanced’ were obtained around 1960. Since then the idea
that signed networks tend over time to become more balanced has been
widely used in several application areas. However, investigation of this
hypothesis has been complicated by the lack of a standard measure of
partial balance, since complete balance is almost never achieved in
practice. We formalize the concept of a measure of partial balance,
discuss various measures, compare the measures on synthetic datasets and
investigate their axiomatic properties. The synthetic data involves
Erd\H{o}s-R\'{e}nyi and specially structured random graphs. We show that
some measures behave better than others in terms of axioms and ability
to differentiate between graphs. We also use well-known data sets from
the sociology and biology literature, such as Read's New Guinean tribes,
gene regulatory networks related to two organisms, and a network
involving senate bill co-sponsorship. Our results show that
substantially different levels of partial balance is observed under
cycle-based, eigenvalue-based and frustration-based measures. We make
some recommendations for measures to be used in future work.}
}



@article{sakhaee2017network,
  title={Network analysis of New Zealand legislation},
  author={Sakhaee, Neda and Wilson, Mark C. and Hendy, Shaun and Zakeri, Golbon},
  journal={New Zealand Law Journal},
  pages={332-337},
  year={2017},
  publisher={},
  keywords={network science},
  url_Paper={},
  abstract={A summary for a legal audience of the basic early findings in Neda's PhD
work about citation networks derived from legislation.}
}



@article{GPW2016,
  title={Networked crowds answer tricky questions poorly},
  author={Girard, Patrick and Pavlov, Valery and Wilson, Mark C.},
  journal={},
  volume={},
  number={},
  pages={8pp},
  year={2016},
  publisher={},
  keywords={network science},
  url_Paper={},
  abstract={We focus on the following basic group decision situation, which we call
iterative distributed jury (IDJ), a variant of the Delphi technique.
Group members seek to answer truthfully a question having a welldefined
objectively correct answer; they revise answers iteratively; only
summary feedback on group members' answers is available at each
iteration; individual estimates are aggregated to form a group answer.
Experimental studies of the effectiveness of Delphi-like methods have
yielded mixed results. To investigate further, we designed a laboratory
multiple choice IDJ experiment having some novel features. One novelty
was that we incentivized participants to reveal their ignorance; another
is the use of both logical and factual questions. We find that, perhaps
surprisingly, substantial social influence occurs even in this highly
anonymized and information-restricted setting, and even for purely
logical questions. Eventual group accuracy is strongly dependent on the
trickiness (likelihood of being answered confidently but wrongly, a
concept distinct from difficulty) of the question. Also, the bulk of
learning occurs by those who were willing to admit to being undecided.
We find that question factors are more important than participant
characteristics.  In addition to consequences for the practical use of
this group decision method, our quantitative results suggest specific
new models of opinion dynamics that deserve detailed study.} 
}



@inproceedings{sakhaee2016new,
  title={New Zealand Legislation Network.},
  author={Sakhaee, Neda and Wilson, Mark C. and Zakeri, Golbon},
  booktitle={JURIX},
  pages={199-202},
  year={2016},
  keywords={network science},
  url_Paper={SWZ2016.pdf},
  abstract={This paper concerns the recently introduced concept of Legislation
Networks, with an application focus on the New Zealand legislation
network. Legislation networks have some novel features which make them
an excellent test case for new network science tools. They involve legal
documents, but differ substantially from citation networks involving
case-law, Supreme Court opinions, etc. We develop several such networks,
compute relevant centrality measures, and apply community detection
algorithms. We study the relationship between the legislation network
measures and legal/political factors. The intention is to follow-up with
more detailed studies in network science (link prediction, node
attribute prediction, generative models and time evolution) and legal
and political analyses.}
}


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J;  and <a class=\"bibbase author link\" href=\"https://markcwilson.site/Research/networks/index.html\">Wilson, M. C.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/1611.09030\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aref-mason-wilson-amodelingandcomputationalstudyofthefrustrationindexinsignednetworks-2020', 'https://arxiv.org/abs/1611.09030', event);\">\n    A modeling and computational study of the frustration index in signed networks.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>Networks</i>, 75(1): 95-110.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/1611.09030\"\n     onclick=\"log_download('aref-mason-wilson-amodelingandcomputationalstudyofthefrustrationindexinsignednetworks-2020', 'https://arxiv.org/abs/1611.09030', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A modeling and computational study of the frustration index in signed networks [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aref-mason-wilson-amodelingandcomputationalstudyofthefrustrationindexinsignednetworks-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aref-mason-wilson-amodelingandcomputationalstudyofthefrustrationindexinsignednetworks-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@Article{aref2020modeling,\n  author    = {Aref, Samin and Mason, Andrew J and Wilson, Mark C.},\n  title     = {A modeling and computational study of the frustration index in signed networks},\n  number    = {1},\n  pages     = {95-110},\n  volume    = {75},\n  abstract  = {Computing the frustration index of a signed graph is a key step toward\nsolving problems in many fields including social networks, political\nscience, physics, chemistry, and biology. The frustration index\ndetermines the distance of a network from a state of total structural\nbalance. Although the definition of the frustration index goes back to\nthe 1950&#x27;s, its exact algorithmic computation, which is closely related\nto classic NP-hard graph problems, has only become a focus in recent\nyears. We develop three new binary linear programming models to compute\nthe frustration index exactly and efficiently as the solution to a\nglobal optimisation problem. Solving the models with prioritised\nbranching and valid inequalities in Gurobi, we can compute the\nfrustration index of real signed networks with over 15000 edges in less\nthan a minute on inexpensive hardware. We provide extensive performance\nanalysis for both random and real signed networks and show that our\nmodels outperform all existing approaches by large factors. Based on\nsolve time, algorithm output, and effective branching factor we\nhighlight the superiority of our models to both exact and heuristic\nmethods in the literature.},\n  journal   = {Networks},\n  keywords  = {network science, computation, graphs},\n  publisher = {Wiley},\n  url_paper = {https://arxiv.org/abs/1611.09030},\n  year      = {2020},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Computing the frustration index of a signed graph is a key step toward solving problems in many fields including social networks, political science, physics, chemistry, and biology. The frustration index determines the distance of a network from a state of total structural balance. Although the definition of the frustration index goes back to the 1950's, its exact algorithmic computation, which is closely related to classic NP-hard graph problems, has only become a focus in recent years. We develop three new binary linear programming models to compute the frustration index exactly and efficiently as the solution to a global optimisation problem. Solving the models with prioritised branching and valid inequalities in Gurobi, we can compute the frustration index of real signed networks with over 15000 edges in less than a minute on inexpensive hardware. We provide extensive performance analysis for both random and real signed networks and show that our models outperform all existing approaches by large factors. Based on solve time, algorithm output, and effective branching factor we highlight the superiority of our models to both exact and heuristic methods in the literature.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sakhaee-wilson-informationextractionframeworktobuildlegislationnetwork-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Sakhaee, N.;  and <a class=\"bibbase author link\" href=\"https://markcwilson.site/Research/networks/index.html\">Wilson, M. C.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sakhaee-wilson-informationextractionframeworktobuildlegislationnetwork-2020', 'https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf', event);\">\n    Information extraction framework to build legislation network.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>Artificial Intelligence and Law</i>,1-24.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf\"\n     onclick=\"log_download('sakhaee-wilson-informationextractionframeworktobuildlegislationnetwork-2020', 'https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Information extraction framework to build legislation network [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sakhaee-wilson-informationextractionframeworktobuildlegislationnetwork-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sakhaee-wilson-informationextractionframeworktobuildlegislationnetwork-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{sakhaee2020information,\n  title={Information extraction framework to build legislation network},\n  author={Sakhaee, Neda and Wilson, Mark C.},\n  journal={Artificial Intelligence and Law},\n  pages={1-24},\n  year={2020},\n  publisher={Springer Netherlands},\n  keywords={network science},\n  url_Paper={https://link.springer.com/content/pdf/10.1007/s10506-020-09263-3.pdf},\n  abstract={This paper concerns an Information Extraction process for building a\ndynamic Legislation Network from legal documents. Unlike supervised\nlearning approaches which require additional calculations, the idea here\nis to apply Information Extraction methodologies by identifying distinct\nexpressions in legal text and extract quality network information. The\nstudy highlights the importance of data accuracy in network analysis and\nimproves approximate string matching techniques for producing reliable\nnetwork datasets with more than 98 percent precision and recall. The\nvalues, applications, and the complexity of the created dynamic\nLegislation Network are also discussed and challenged.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper concerns an Information Extraction process for building a dynamic Legislation Network from legal documents. Unlike supervised learning approaches which require additional calculations, the idea here is to apply Information Extraction methodologies by identifying distinct expressions in legal text and extract quality network information. The study highlights the importance of data accuracy in network analysis and improves approximate string matching techniques for producing reliable network datasets with more than 98 percent precision and recall. The values, applications, and the complexity of the created dynamic Legislation Network are also discussed and challenged.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"aref-wilson-balanceandfrustrationinsignednetworks-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Aref, S.;  and <a class=\"bibbase author link\" href=\"https://markcwilson.site/Research/networks/index.html\">Wilson, M. C.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://academic.oup.com/comnet/article-abstract/7/2/163/5074195\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aref-wilson-balanceandfrustrationinsignednetworks-2019', 'https://academic.oup.com/comnet/article-abstract/7/2/163/5074195', event);\">\n    Balance and frustration in signed networks.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>Journal of Complex Networks</i>, 7(2): 163-189.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://academic.oup.com/comnet/article-abstract/7/2/163/5074195\"\n     onclick=\"log_download('aref-wilson-balanceandfrustrationinsignednetworks-2019', 'https://academic.oup.com/comnet/article-abstract/7/2/163/5074195', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Balance and frustration in signed networks [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aref-wilson-balanceandfrustrationinsignednetworks-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aref-wilson-balanceandfrustrationinsignednetworks-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{aref2019balance,\n  title={Balance and frustration in signed networks},\n  author={Aref, Samin and Wilson, Mark C.},\n  journal={Journal of Complex Networks},\n  volume={7},\n  number={2},\n  pages={163-189},\n  year={2019},\n  publisher={Oxford University Press},\n  keywords={network science, graphs},\n  url_Paper= {https://academic.oup.com/comnet/article-abstract/7/2/163/5074195},\n  abstract={The frustration index is a key measure for analysing signed networks,\nwhich has been underused due to its computational complexity. We use an\nexact optimisation-based method to analyse frustration as a global\nstructural property of signed networks coming from diverse application\nareas. In the classic friend-enemy interpretation of balance theory, a\nby-product of computing the frustration index is the partitioning of\nnodes into two internally solidary but mutually hostile groups. The main\npurpose of this paper is to present general methodology for answering\nquestions related to partial balance in signed networks, and apply it to\na range of representative examples that are now analysable because of\nadvances in computational methods. We provide exact numerical results on\nsocial and biological signed networks, networks of formal alliances and\nantagonisms between countries, and financial portfolio networks.\nMolecular graphs of carbon and Ising models are also considered. We\npoint out several mistakes in the signed networks literature caused by\ninaccurate computation, implementation errors or inappropriate\nmeasures.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The frustration index is a key measure for analysing signed networks, which has been underused due to its computational complexity. We use an exact optimisation-based method to analyse frustration as a global structural property of signed networks coming from diverse application areas. In the classic friend-enemy interpretation of balance theory, a by-product of computing the frustration index is the partitioning of nodes into two internally solidary but mutually hostile groups. The main purpose of this paper is to present general methodology for answering questions related to partial balance in signed networks, and apply it to a range of representative examples that are now analysable because of advances in computational methods. We provide exact numerical results on social and biological signed networks, networks of formal alliances and antagonisms between countries, and financial portfolio networks. Molecular graphs of carbon and Ising models are also considered. We point out several mistakes in the signed networks literature caused by inaccurate computation, implementation errors or inappropriate measures.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"aref-mason-wilson-computingthelineindexofbalanceusingintegerprogrammingoptimisation-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Aref, S.; Mason, A. J;  and <a class=\"bibbase author link\" href=\"https://markcwilson.site/Research/networks/index.html\">Wilson, M. C.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/1710.09876\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aref-mason-wilson-computingthelineindexofbalanceusingintegerprogrammingoptimisation-2018', 'https://arxiv.org/abs/1710.09876', event);\">\n    Computing the line index of balance using integer programming optimisation.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Optimization Problems in Graph Theory</i>, pages 65-84. Springer, Cham,  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/1710.09876\"\n     onclick=\"log_download('aref-mason-wilson-computingthelineindexofbalanceusingintegerprogrammingoptimisation-2018', 'https://arxiv.org/abs/1710.09876', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Computing the line index of balance using integer programming optimisation [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aref-mason-wilson-computingthelineindexofbalanceusingintegerprogrammingoptimisation-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aref-mason-wilson-computingthelineindexofbalanceusingintegerprogrammingoptimisation-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{aref2018computing,\n  title={Computing the line index of balance using integer programming optimisation},\n  author={Aref, Samin and Mason, Andrew J and Wilson, Mark C.},\n  booktitle={Optimization Problems in Graph Theory},\n  pages={65-84},\n  year={2018},\n  publisher={Springer, Cham},\n  keywords={network science, computation, graphs},\n  url_Paper={https://arxiv.org/abs/1710.09876},\n  abstract={An important measure of signed graphs is the line index of balance which\nhas several applications in many fields. However, this graph-theoretic\nmeasure was underused for decades because of the inherent complexity in\nits computation which is closely related to solving NP-hard graph\noptimisation problems like MAXCUT. We develop new quadratic and linear\nprogramming models to compute the line index of balance exactly. Using\nthe Gurobi integer programming optimisation solver, we evaluate the line\nindex of balance on real-world and synthetic datasets. The synthetic\ndata involves Erd\\H{o}s-R\\&#x27;{e}nyi graphs, Barab\\&#x27;{a}si-Albert graphs,\nand specially structured random graphs. We also use well known datasets\nfrom the sociology literature, such as signed graphs inferred from\nstudents&#x27; choice and rejection as well as datasets from the biology\nliterature including gene regulatory networks. The results show that\nexact values of the line index of balance in relatively large signed\ngraphs can be efficiently computed using our suggested optimisation\nmodels. We find that most real-world social networks and some biological\nnetworks have small line index of balance which indicates that they are\nclose to balanced.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  An important measure of signed graphs is the line index of balance which has several applications in many fields. However, this graph-theoretic measure was underused for decades because of the inherent complexity in its computation which is closely related to solving NP-hard graph optimisation problems like MAXCUT. We develop new quadratic and linear programming models to compute the line index of balance exactly. Using the Gurobi integer programming optimisation solver, we evaluate the line index of balance on real-world and synthetic datasets. The synthetic data involves Erdős-Rényi graphs, Barabási-Albert graphs, and specially structured random graphs. We also use well known datasets from the sociology literature, such as signed graphs inferred from students' choice and rejection as well as datasets from the biology literature including gene regulatory networks. The results show that exact values of the line index of balance in relatively large signed graphs can be efficiently computed using our suggested optimisation models. We find that most real-world social networks and some biological networks have small line index of balance which indicates that they are close to balanced.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"aref-wilson-measuringpartialbalanceinsignednetworks-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Aref, S.;  and <a class=\"bibbase author link\" href=\"https://markcwilson.site/Research/networks/index.html\">Wilson, M. C.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/1509.04037\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aref-wilson-measuringpartialbalanceinsignednetworks-2018', 'http://arxiv.org/abs/1509.04037', event);\">\n    Measuring partial balance in signed networks.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>Journal of Complex Networks</i>, 6(4): 566-595.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/1509.04037\"\n     onclick=\"log_download('aref-wilson-measuringpartialbalanceinsignednetworks-2018', 'http://arxiv.org/abs/1509.04037', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Measuring partial balance in signed networks [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aref-wilson-measuringpartialbalanceinsignednetworks-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aref-wilson-measuringpartialbalanceinsignednetworks-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{aref2018measuring,\n  title={Measuring partial balance in signed networks},\n  author={Aref, Samin and Wilson, Mark C.},\n  journal={Journal of Complex Networks},\n  volume={6},\n  number={4},\n  pages={566-595},\n  year={2018},\n  publisher={Oxford University Press},\n  keywords={network science, graphs},\n  url_Paper={http://arxiv.org/abs/1509.04037},\n  abstract={Is the enemy of an enemy necessarily a friend? If not, to what extent\ndoes this tend to hold? Such questions were formulated in terms of\nsigned (social) networks and necessary and sufficient conditions for a\nnetwork to be ‘balanced’ were obtained around 1960. Since then the idea\nthat signed networks tend over time to become more balanced has been\nwidely used in several application areas. However, investigation of this\nhypothesis has been complicated by the lack of a standard measure of\npartial balance, since complete balance is almost never achieved in\npractice. We formalize the concept of a measure of partial balance,\ndiscuss various measures, compare the measures on synthetic datasets and\ninvestigate their axiomatic properties. The synthetic data involves\nErd\\H{o}s-R\\&#x27;{e}nyi and specially structured random graphs. We show that\nsome measures behave better than others in terms of axioms and ability\nto differentiate between graphs. We also use well-known data sets from\nthe sociology and biology literature, such as Read&#x27;s New Guinean tribes,\ngene regulatory networks related to two organisms, and a network\ninvolving senate bill co-sponsorship. Our results show that\nsubstantially different levels of partial balance is observed under\ncycle-based, eigenvalue-based and frustration-based measures. We make\nsome recommendations for measures to be used in future work.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Is the enemy of an enemy necessarily a friend? If not, to what extent does this tend to hold? Such questions were formulated in terms of signed (social) networks and necessary and sufficient conditions for a network to be ‘balanced’ were obtained around 1960. Since then the idea that signed networks tend over time to become more balanced has been widely used in several application areas. However, investigation of this hypothesis has been complicated by the lack of a standard measure of partial balance, since complete balance is almost never achieved in practice. We formalize the concept of a measure of partial balance, discuss various measures, compare the measures on synthetic datasets and investigate their axiomatic properties. The synthetic data involves Erdős-Rényi and specially structured random graphs. We show that some measures behave better than others in terms of axioms and ability to differentiate between graphs. We also use well-known data sets from the sociology and biology literature, such as Read's New Guinean tribes, gene regulatory networks related to two organisms, and a network involving senate bill co-sponsorship. Our results show that substantially different levels of partial balance is observed under cycle-based, eigenvalue-based and frustration-based measures. We make some recommendations for measures to be used in future work.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"sakhaee-wilson-hendy-zakeri-networkanalysisofnewzealandlegislation-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Sakhaee, N.; <a class=\"bibbase author link\" href=\"https://markcwilson.site/Research/networks/index.html\">Wilson, M. C.</a>; Hendy, S.;  and Zakeri, G.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://drive.google.com/uc?export=download&amp;id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sakhaee-wilson-hendy-zakeri-networkanalysisofnewzealandlegislation-2017', 'https://drive.google.com/uc?export=download&amp;id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF', event);\">\n    Network analysis of New Zealand legislation.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>New Zealand Law Journal</i>,332-337.  2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://drive.google.com/uc?export=download&amp;id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF\"\n     onclick=\"log_download('sakhaee-wilson-hendy-zakeri-networkanalysisofnewzealandlegislation-2017', 'https://drive.google.com/uc?export=download&amp;id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Network analysis of New Zealand legislation [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sakhaee-wilson-hendy-zakeri-networkanalysisofnewzealandlegislation-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sakhaee-wilson-hendy-zakeri-networkanalysisofnewzealandlegislation-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{sakhaee2017network,\n  title={Network analysis of New Zealand legislation},\n  author={Sakhaee, Neda and Wilson, Mark C. and Hendy, Shaun and Zakeri, Golbon},\n  journal={New Zealand Law Journal},\n  pages={332-337},\n  year={2017},\n  publisher={},\n  keywords={network science},\n  url_Paper={},\n  abstract={A summary for a legal audience of the basic early findings in Neda&#x27;s PhD\nwork about citation networks derived from legislation.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  A summary for a legal audience of the basic early findings in Neda's PhD work about citation networks derived from legislation.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"girard-pavlov-wilson-networkedcrowdsanswertrickyquestionspoorly-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Girard, P.; Pavlov, V.;  and <a class=\"bibbase author link\" href=\"https://markcwilson.site/Research/networks/index.html\">Wilson, M. C.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://drive.google.com/uc?export=download&amp;id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'girard-pavlov-wilson-networkedcrowdsanswertrickyquestionspoorly-2016', 'https://drive.google.com/uc?export=download&amp;id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF', event);\">\n    Networked crowds answer tricky questions poorly.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  ,8pp.  2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://drive.google.com/uc?export=download&amp;id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF\"\n     onclick=\"log_download('girard-pavlov-wilson-networkedcrowdsanswertrickyquestionspoorly-2016', 'https://drive.google.com/uc?export=download&amp;id=1NEXsxwRAx2CWt43v0y0jyZdP1LdOS_xF', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Networked crowds answer tricky questions poorly [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/girard-pavlov-wilson-networkedcrowdsanswertrickyquestionspoorly-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('girard-pavlov-wilson-networkedcrowdsanswertrickyquestionspoorly-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{GPW2016,\n  title={Networked crowds answer tricky questions poorly},\n  author={Girard, Patrick and Pavlov, Valery and Wilson, Mark C.},\n  journal={},\n  volume={},\n  number={},\n  pages={8pp},\n  year={2016},\n  publisher={},\n  keywords={network science},\n  url_Paper={},\n  abstract={We focus on the following basic group decision situation, which we call\niterative distributed jury (IDJ), a variant of the Delphi technique.\nGroup members seek to answer truthfully a question having a welldefined\nobjectively correct answer; they revise answers iteratively; only\nsummary feedback on group members&#x27; answers is available at each\niteration; individual estimates are aggregated to form a group answer.\nExperimental studies of the effectiveness of Delphi-like methods have\nyielded mixed results. To investigate further, we designed a laboratory\nmultiple choice IDJ experiment having some novel features. One novelty\nwas that we incentivized participants to reveal their ignorance; another\nis the use of both logical and factual questions. We find that, perhaps\nsurprisingly, substantial social influence occurs even in this highly\nanonymized and information-restricted setting, and even for purely\nlogical questions. Eventual group accuracy is strongly dependent on the\ntrickiness (likelihood of being answered confidently but wrongly, a\nconcept distinct from difficulty) of the question. Also, the bulk of\nlearning occurs by those who were willing to admit to being undecided.\nWe find that question factors are more important than participant\ncharacteristics.  In addition to consequences for the practical use of\nthis group decision method, our quantitative results suggest specific\nnew models of opinion dynamics that deserve detailed study.} \n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We focus on the following basic group decision situation, which we call iterative distributed jury (IDJ), a variant of the Delphi technique. Group members seek to answer truthfully a question having a welldefined objectively correct answer; they revise answers iteratively; only summary feedback on group members' answers is available at each iteration; individual estimates are aggregated to form a group answer. Experimental studies of the effectiveness of Delphi-like methods have yielded mixed results. To investigate further, we designed a laboratory multiple choice IDJ experiment having some novel features. One novelty was that we incentivized participants to reveal their ignorance; another is the use of both logical and factual questions. We find that, perhaps surprisingly, substantial social influence occurs even in this highly anonymized and information-restricted setting, and even for purely logical questions. Eventual group accuracy is strongly dependent on the trickiness (likelihood of being answered confidently but wrongly, a concept distinct from difficulty) of the question. Also, the bulk of learning occurs by those who were willing to admit to being undecided. We find that question factors are more important than participant characteristics. In addition to consequences for the practical use of this group decision method, our quantitative results suggest specific new models of opinion dynamics that deserve detailed study.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sakhaee-wilson-zakeri-newzealandlegislationnetwork-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  Sakhaee, N.; <a class=\"bibbase author link\" href=\"https://markcwilson.site/Research/networks/index.html\">Wilson, M. C.</a>;  and Zakeri, G.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://drive.google.com/SWZ2016.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sakhaee-wilson-zakeri-newzealandlegislationnetwork-2016', 'https://drive.google.com/SWZ2016.pdf', event);\">\n    New Zealand Legislation Network.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>JURIX</i>, pages 199-202,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://drive.google.com/SWZ2016.pdf\"\n     onclick=\"log_download('sakhaee-wilson-zakeri-newzealandlegislationnetwork-2016', 'https://drive.google.com/SWZ2016.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"New Zealand Legislation Network. [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sakhaee-wilson-zakeri-newzealandlegislationnetwork-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sakhaee-wilson-zakeri-newzealandlegislationnetwork-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{sakhaee2016new,\n  title={New Zealand Legislation Network.},\n  author={Sakhaee, Neda and Wilson, Mark C. and Zakeri, Golbon},\n  booktitle={JURIX},\n  pages={199-202},\n  year={2016},\n  keywords={network science},\n  url_Paper={SWZ2016.pdf},\n  abstract={This paper concerns the recently introduced concept of Legislation\nNetworks, with an application focus on the New Zealand legislation\nnetwork. Legislation networks have some novel features which make them\nan excellent test case for new network science tools. They involve legal\ndocuments, but differ substantially from citation networks involving\ncase-law, Supreme Court opinions, etc. We develop several such networks,\ncompute relevant centrality measures, and apply community detection\nalgorithms. We study the relationship between the legislation network\nmeasures and legal/political factors. The intention is to follow-up with\nmore detailed studies in network science (link prediction, node\nattribute prediction, generative models and time evolution) and legal\nand political analyses.}\n}\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper concerns the recently introduced concept of Legislation Networks, with an application focus on the New Zealand legislation network. Legislation networks have some novel features which make them an excellent test case for new network science tools. They involve legal documents, but differ substantially from citation networks involving case-law, Supreme Court opinions, etc. We develop several such networks, compute relevant centrality measures, and apply community detection algorithms. We study the relationship between the legislation network measures and legal/political factors. The intention is to follow-up with more detailed studies in network science (link prediction, node attribute prediction, generative models and time evolution) and legal and political analyses.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);