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dc.contributor.authorAlabdulkareem, Ahmad
dc.contributor.authorYuan, Yuan
dc.contributor.authorPentland, Alex Paul
dc.date.accessioned2019-03-07T19:23:20Z
dc.date.available2019-03-07T19:23:20Z
dc.date.issued2018-11
dc.identifier.issn2041-1723
dc.identifier.urihttp://hdl.handle.net/1721.1/120819
dc.description.abstractUnderstanding the mechanisms of network formation is central in social network analysis. Network formation has been studied in many research fields with their different focuses; for example, network embedding algorithms in machine learning literature consider broad heterogeneity among agents while the social sciences emphasize the interpretability of link formation mechanisms. Here we propose a social network formation model that integrates methods in multiple disciplines and retain both heterogeneity and interpretability. We represent each agent by an “endowment vector” that encapsulates their features and use game-theoretical methods to model the utility of link formation. After applying machine learning methods, we further analyze our model by examining micro- and macro- level properties of social networks as most agent-based models do. Our work contributes to the literature on network formation by combining the methods in game theory, agent-based modeling, machine learning, and computational sociology.en_US
dc.description.sponsorshipKing Abdulaziz City of Science and Technology (Saudia Arabia)en_US
dc.description.sponsorshipMIT Trust Data Consortiumen_US
dc.publisherNature Publishing Groupen_US
dc.relation.isversionofhttp://dx.doi.org/10.1038/s41467-018-07089-xen_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceNatureen_US
dc.titleAn interpretable approach for social network formation among heterogeneous agentsen_US
dc.typeArticleen_US
dc.identifier.citationYuan, Yuan, Ahmad Alabdulkareem, and Alex “Sandy” Pentland. “An Interpretable Approach for Social Network Formation Among Heterogeneous Agents.” Nature Communications 9, no. 1 (November 8, 2018). © 2018 The Authorsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.mitauthorYuan, Yuan
dc.contributor.mitauthorPentland, Alex Paul
dc.relation.journalNature Communicationsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-03-01T14:03:44Z
dspace.orderedauthorsYuan, Yuan; Alabdulkareem, Ahmad; Pentland, Alex ‘Sandy’en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8053-9983
mit.licensePUBLISHER_CCen_US


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