Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure
This work addresses the challenge of understanding complex network dynamics for researchers and practitioners in network analysis, though it appears incremental as it builds on existing edge partition and hierarchical modeling approaches.
The paper tackles the problem of modeling evolving latent communities in temporal networks by proposing a dynamic network model that decomposes edges using a Poisson-gamma edge partition model with nonnegative vertex-community memberships, and it demonstrates effectiveness in uncovering interpretable structures and surpassing state-of-the-art models in link prediction and community detection tasks.
We propose a novel dynamic network model to capture evolving latent communities within temporal networks. To achieve this, we decompose each observed dynamic edge between vertices using a Poisson-gamma edge partition model, assigning each vertex to one or more latent communities through \emph{nonnegative} vertex-community memberships. Specifically, hierarchical transition kernels are employed to model the interactions between these latent communities in the observed temporal network. A hierarchical graph prior is placed on the transition structure of the latent communities, allowing us to model how they evolve and interact over time. Consequently, our dynamic network enables the inferred community structure to merge, split, and interact with one another, providing a comprehensive understanding of complex network dynamics. Experiments on various real-world network datasets demonstrate that the proposed model not only effectively uncovers interpretable latent structures but also surpasses other state-of-the art dynamic network models in the tasks of link prediction and community detection.