Seminar by Weijing Tang

Statistics and Biostatistics seminar series

Weijing Tang
Carnegie Mellon University

Held virtually on Zoom Meetings.

Watch the talk in M3 3127.

Inference and Learning for Signed Networks Guided by Social Theory

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 latent variable models for signed networks that accommodate the balance theory for embedding learning and community detection.