A0408
Title: Community detection with heterogeneous block covariance model
Authors: Yunpeng Zhao - Colorado State University (United States) [presenting]
Abstract: Community detection is the task of clustering objects based on their pairwise relationships. Most of the model-based community detection methods, such as the stochastic block model and its variants, are designed for networks with binary (yes/no) edges. In many practical scenarios, edges often possess continuous weights, spanning positive and negative values, which reflect varying levels of connectivity. To address this challenge, the heterogeneous block covariance model (HBCM) is introduced, which defines a community structure within the covariance matrix where edges have signed and continuous weights. Furthermore, it takes into account the heterogeneity of objects when forming connections with other objects within a community. A novel variational expectation-maximization algorithm is proposed to estimate the group membership. The HBCM provides provable consistent estimates of memberships, and its promising performance is observed in numerical simulations with different setups. The model is applied to a yeast gene expression dataset to detect the gene clusters regulated by different transcript factors during the yeast cell cycle.
