Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Models
This work addresses the challenge of interpreting complex community structures in hypergraphs with attributes, which is incremental as it builds on existing models to improve visualization.
The authors tackled the problem of visualizing and interpreting community structure in attributed hypergraphs by proposing HyperNEO, a framework that combines mixed-membership stochastic block models with dimensionality reduction to generate node layouts preserving community memberships, evaluated on synthetic and empirical data.
Hypergraphs represent complex systems involving interactions among more than two entities and allow the investigation of higher-order structure and dynamics in complex systems. Node attribute data, which often accompanies network data, can enhance the inference of community structure in complex systems. While mixed-membership stochastic block models have been employed to infer community structure in hypergraphs, they complicate the visualization and interpretation of inferred community structure by assuming that nodes may possess soft community memberships. In this study, we propose a framework, HyperNEO, that combines mixed-membership stochastic block models for hypergraphs with dimensionality reduction methods. Our approach generates a node layout that largely preserves the community memberships of nodes. We evaluate our framework on both synthetic and empirical hypergraphs with node attributes. We expect our framework will broaden the investigation and understanding of higher-order community structure in complex systems.