An interpretable approach for social network formation among heterogeneous agents
Understanding 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 t...
| Main Authors: | , , |
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| Format: | Article |
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Nature Publishing Group
2019
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| Online Access: | http://hdl.handle.net/1721.1/120819 https://orcid.org/0000-0002-8053-9983 |
| _version_ | 1826199485220388864 |
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| author | Alabdulkareem, Ahmad Yuan, Yuan Pentland, Alex Paul |
| author2 | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| author_facet | Massachusetts Institute of Technology. Institute for Data, Systems, and Society Alabdulkareem, Ahmad Yuan, Yuan Pentland, Alex Paul |
| author_sort | Alabdulkareem, Ahmad |
| collection | MIT |
| description | Understanding 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. |
| first_indexed | 2024-09-23T11:20:38Z |
| format | Article |
| id | mit-1721.1/120819 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:20:38Z |
| publishDate | 2019 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/1208192022-09-27T18:55:05Z An interpretable approach for social network formation among heterogeneous agents Alabdulkareem, Ahmad Yuan, Yuan Pentland, Alex Paul Massachusetts Institute of Technology. Institute for Data, Systems, and Society Yuan, Yuan Pentland, Alex Paul Understanding 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. King Abdulaziz City of Science and Technology (Saudia Arabia) MIT Trust Data Consortium 2019-03-07T19:23:20Z 2019-03-07T19:23:20Z 2018-11 2019-03-01T14:03:44Z Article http://purl.org/eprint/type/JournalArticle 2041-1723 http://hdl.handle.net/1721.1/120819 Yuan, 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 Authors https://orcid.org/0000-0002-8053-9983 http://dx.doi.org/10.1038/s41467-018-07089-x Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Nature Publishing Group Nature |
| spellingShingle | Alabdulkareem, Ahmad Yuan, Yuan Pentland, Alex Paul An interpretable approach for social network formation among heterogeneous agents |
| title | An interpretable approach for social network formation among heterogeneous agents |
| title_full | An interpretable approach for social network formation among heterogeneous agents |
| title_fullStr | An interpretable approach for social network formation among heterogeneous agents |
| title_full_unstemmed | An interpretable approach for social network formation among heterogeneous agents |
| title_short | An interpretable approach for social network formation among heterogeneous agents |
| title_sort | interpretable approach for social network formation among heterogeneous agents |
| url | http://hdl.handle.net/1721.1/120819 https://orcid.org/0000-0002-8053-9983 |
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