Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block Models

Abstract

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.

References in corpus (26)

• Fast unfolding of communities in large networks
• Community structure in social and biological networks
• Finding and evaluating community structure in networks
• Community detection in graphs
• Stochastic blockmodels and community structure in networks
• Community detection in networks: A user guide
• Networks beyond pairwise interactions: structure and dynamics
• High-resolution measurements of face-to-face contact patterns in a primary school
• Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
• The ground truth about metadata and community detection in networks
• Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors
• Structure and inference in annotated networks
• An efficient and principled method for detecting communities in networks
• Consistency of spectral clustering in stochastic block models
• World citation and collaboration networks: uncovering the role of geography in science
• Mitigation of infectious disease at school: targeted class closure vs school closure
• A Review of Stochastic Block Models and Extensions for Graph Clustering
• Data on face-to-face contacts in an office building suggests a low-cost vaccination strategy based on community linkers
• Community detection, link prediction, and layer interdependence in multilayer networks
• Inference of hyperedges and overlapping communities in hypergraphs
• Clustering via Hypergraph Modularity
• Community Detection in Large Hypergraphs
• Community detection with node attributes in multilayer networks
• A class of models for random hypergraphs
• Quantifying gender imbalance in East Asian academia: Research career and citation practice
• Structure and inference in hypergraphs with node attributes