2026-09-20T02:48:07Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1590952025-10-16T03:23:22Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Jadbabaie, Ali
Sarker, Arnab Kumar
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
2025-04-14T14:05:17Z
2025-04-14T14:05:17Z
2025-02
2025-03-24T19:29:14.696Z
https://hdl.handle.net/1721.1/159095
https://orcid.org/0000-0003-1680-9421
The de facto representation of a social network is a graph— individuals are represented as nodes, and relationships between pairs of individuals are represented as edges. This results in a powerful abstraction by which social relationships can be systematically studied to understand emergent population-scale behavior. However, many social interactions occur in groups: three individuals may co-author a paper, a team of employees may collaborate on a task, a single tweet may mention four users. Breaking such interactions into a collection of pairwise relationships can oversimplify the rich social contexts in which these individuals know one another. This thesis explores a different paradigm of social network analysis, namely, using "higher-order" network models such as hypergraphs and simplicial complexes which can explicitly encode co-present contexts between three or more individuals. The first two projects describe how higher-order interactions can differ from pairwise interactions in terms of micro-level content and macro-level structure, respectively. The latter two projects then develop an applied mathematical toolkit for the algebraic topological analysis of higher-order interactions in social networks.
Ph.D.
Massachusetts Institute of Technology
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Higher-Order Interactions in Social Systems
Thesis
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Doctoral
Doctor of Philosophy
Publication
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<Abstract>The de facto representation of a social network is a graph— individuals are represented as nodes, and relationships between pairs of individuals are represented as edges. This results in a powerful abstraction by which social relationships can be systematically studied to understand emergent population-scale behavior. However, many social interactions occur in groups: three individuals may co-author a paper, a team of employees may collaborate on a task, a single tweet may mention four users. Breaking such interactions into a collection of pairwise relationships can oversimplify the rich social contexts in which these individuals know one another. This thesis explores a different paradigm of social network analysis, namely, using "higher-order" network models such as hypergraphs and simplicial complexes which can explicitly encode co-present contexts between three or more individuals. The first two projects describe how higher-order interactions can differ from pairwise interactions in terms of micro-level content and macro-level structure, respectively. The latter two projects then develop an applied mathematical toolkit for the algebraic topological analysis of higher-order interactions in social networks.</Abstract>
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