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dc.contributor.advisorAlex Pentland.en_US
dc.contributor.authorLeng, Yanen_US
dc.contributor.otherProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.date.accessioned2020-09-15T22:01:13Z
dc.date.available2020-09-15T22:01:13Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127502
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 171-186).en_US
dc.description.abstractIndividuals form network connections based on homophily; individuals' networks also shape their actions. Pervasive behavioral data provides opportunities for a richer view of the decisions on networks. Yet, the increasing volume, complex structures, and dynamics of behavioral data stretch the limit of conventional methods. I develop mathematical modeling (e.g., machine learning, game theory, and network science) and large-scale behavioral data to study collective behaviors over social networks. My dissertation will tackle this area in four directions, revolving around the intricate linkage between individuals' characteristics, actions, and their networks. First, I empirically investigate how social influence spreads over networks using two massive cell phone data, and theoretically model how do individuals aggregate information from local neighbors. Second, I study how to leverage influential nodes for selective network interventions (e.g., marketing and political campaigns), by proposing a centrality measure going beyond network structures. Third, I build a geometric deep learning model to infer individual preferences and make personalized recommendations to utilize noisy network information and nodal features effectively. Last, given that the network is essential, I develop a framework to infer the network connections based on observed actions, when networks are unavailable. My thesis provides building blocks for further network-based machine learning problems integrating nodal heterogeneity and network structures. Moreover, the findings on human behaviors and frameworks developed in my thesis shed light on marketing campaigns and population management.en_US
dc.description.statementofresponsibilityby Yan Leng.en_US
dc.format.extent209 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectProgram in Media Arts and Sciencesen_US
dc.titleCollective behavior over social networks with data-driven and machine learning modelsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.identifier.oclc1193026890en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciencesen_US
dspace.imported2020-09-15T22:01:13Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentMediaen_US


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