Community Detection for Signed Networks

Music City Center 

Community detection, discovering the underlying communities within a network from observed connections, is a fundamental problem in network analysis, which has been extensively studied across various domains. In the context of signed networks, not only the connections but also their signs play a crucial role in community identification. Particularly, the empirical evidence of balance theory in real-world signed networks makes it a compelling property for this purpose. In this work, we propose a novel balanced stochastic block model, which has a hierarchical community structure induced by balance theory. We also develop a fast maximum pseudo likelihood estimation approach for community detection with exact recovery. Our proposed method is used to detect meaningful node clusters for downstream applications.

community detection

signed networks

stochastic block model 

Section on Statistical Learning and Data Science