{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/159095"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/159095","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Higher-Order Interactions in Social Systems","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_html":"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_has_math":false,"creators":["Sarker, Arnab Kumar"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Institute for Data, Systems, and Society","school":null,"contributors":[],"advisors":["Jadbabaie, Ali"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02","date_published":"2025-02","updated_at":"2026-07-22T22:21:52Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/159095","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Jadbabaie, Ali"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Higher-Order Interactions in Social Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Jadbabaie, Ali"],"dc:contributor.department":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society"],"dc:creator":["Sarker, Arnab Kumar"],"dc:date.accessioned":["2025-04-14T14:05:17Z"],"dc:date.available":["2025-04-14T14:05:17Z"],"dc:date.issued":["2025-02"],"dc:description.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. 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