Structure, dynamics, and inference in networks

Networks offer a unified, conceptual formalism for reasoning about complex, relational systems. While pioneering work in network science focused primarily on the ability of "universal" models to explain the features of observed systems, contemporary research increasingly focuses on challenges and opportunities for data analysis in complex systems. In this thesis we study four problems, each of which is informed by the need for theory-informed modeling in network data science. The first chapter is a study of binary-state adaptive voter models (AVMs). AVMs model the emergence of global opinion-based network polarization from localized decision-making, doing so through a simple coupling of node and edge states. This coupling yields rich behavior, including phase transitions and low-dimensional quasistable manifolds. However, the coupling also makes these models extremely difficult to analyze.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Chodrow, Philip S.(Philip Samuel)

Advisor dc:contributor.advisor

• Patrick Jaillet and Marta González.

• Operations Research Center.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/128994

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/128994