Modeling and Analysis of Social Network Dynamics: Propagation, Learning and Structural Balance
Abstract
Network is a natural physical model of social systems and an important tool to understand various dynamical phenomena in human groups and societies. Network dynamics is a powerful theoretical approach to study how local interactions among individuals lead to certain macroscopic phenomena, and the role of network structure in such dynamical processes. In this thesis, we model and analyze the following two aspects of social network dynamics: dynamics on networks and dynamics of networks. The former means the evolution of individual states via social interactions, while the latter refers to the evolution of the social relations themselves. Regarding the dynamics on social networks, we focus on the modeling and analysis of network propagation processes. Firstly, we review a class of deterministic nonlinear models for the propagation of infectious diseases over contact networks. For each model setting, we provide a comprehensive nonlinear analysis including both known and novel results. Secondly, we propose a class of stochastic propagation models for multiple competing products over a social network, and study their mean-field approximations. Two types of games based on the mean-field competitive propagation models are proposed and the quality-seeding trade-off is investigated. Finally, we apply the general idea of social influence to an engineering sensors system and study the sequential decision aggregation with social pressure. For the dynamics of social networks, we study the evolution of the interpersonal appraisal networks and its emergent collective behavior. Firstly, we proposes models of learning processes in teams of individuals collectively executing a sequence of tasks. The closely-related proposed models have increasing complexity, starting with a centralized assignment and learning model, and finishing with a social model of interpersonal appraisal, assignment, learning and influence.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 30, 2018
- Accession Number
- AD1228827
Entities
People
- Wenjun Mei
Organizations
- University of California, Santa Barbara