C-SoDA Lecture Series presents: Weijing Tang

Title: Population-Level Balance in Signed Networks

Abstract: Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for signed networks have been largely unexplored. In signed networks, there exist both positive (e.g., like, trust) and negative (e.g., dislike, distrust) edges, which are commonly seen in real-world scenarios. The positive and negative edges in signed networks lead to unique structural patterns, which pose challenges for statistical modeling. In this talk, we introduce a general latent space framework for modeling signed networks and accommodating the well known balance theory, i.e., "the enemy of my enemy is my friend'' and "the friend of my friend is my friend''. The proposed approach treats both edges and their signs as random variables, and characterizes the balance theory with a novel and natural notion of population-level balance. This approach guides us towards building a class of balanced inner-product models, and towards developing scalable algorithms via projected gradient descent to estimate the latent variables. We also establish non-asymptotic error rates for the estimates. In addition, we apply the proposed approach to an international relation network, which provides an informative and interpretable model-based visualization of countries during World War II.

Bio: 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.