A Bayesian Non-parametric Framework for Community Detection in Multi-way Interaction Network

Oregon Convention Center 

Community detection is a fundamental task in network analysis. Learning underlying network structures has brought deep insights into the understanding of complex systems. Real network data often arise via a series of interactions, with each interaction involving more than two nodes, e.g. multi-way interaction. The block components differ by different interactions. While many methods have focused on clustering nodes into blocks, few account for the fact that interactions may exhibit clustering as well. In this project, we introduce a Bayesian non-parametric framework to study multi-way interaction networks with joint modeling of latent node-level block labels and latent interaction-level labels. We will discuss challenges regarding the identifiability of latent labels in this framework and show the demonstration in simulated data. A Gibbs sampling-based algorithm is derived. We will conclude the presentation with the application of our proposed method to the Medicare claim data over the years and the potential medical implications for future.

Community Detection in Network data

Bayesian non-parametric framework

Latent Class Model 

Section on Statistical Graphics