Weijing Tang

Session: Morning Scientific Session - Advances in Social Network Analysis
Title: Population-Level Balance in Signed Networks
Abstract: In many real-world networks,  relationships often go beyond simple presence or absence; they can be positive (e.g., friendship, alliance, and mutualism) or negative (e.g., enmity, disputes, and competition). These negative relationships display substantially different properties from positive ones, and more importantly, their presence interacts in unique ways. The balance theory originating from social psychology, illustrated by proverbs like "a friend of my friend is my friend'' and "an enemy of my enemy is my friend'', provides insight into the formation mechanism of positive and negative connections. In this talk, we characterize the balance theory with a novel and natural notion of population-level balance. We propose a nonparametric inference method to evaluate the real-world evidence of population-level balance in signed networks. Inspired by the empirical findings, we further develop a general latent space framework for modeling signed networks while accommodating the balance theory. 

Weijing Tang is an Assistant Professor in Statistics and Data Science at Carnegie Mellon University. She has been working on developing statistical methodology and theory for network analysis, machine learning, and survival analysis with applications to health and social sciences. Weijing Tang's website.