2026-09-18T23:42:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1275022026-06-16T18:14:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Alex Pentland.
Leng, Yan
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2020-09-15T22:01:13Z
2020-09-15T22:01:13Z
2020
2020
https://hdl.handle.net/1721.1/127502
1193026890
Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 171-186).
Individuals 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.
by Yan Leng.
Ph.D.
Ph.D. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences
209 pages
eng
Massachusetts Institute of Technology
MIT 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.
http://dspace.mit.edu/handle/1721.1/7582
Program in Media Arts and Sciences
Collective behavior over social networks with data-driven and machine learning models
Thesis
application/pdf
2020-09-15T22:01:13Z
Publication
Doctoral
Media
unknown
unknown
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