Hypergraph Clustering: A Modularity Maximization Approach

Understand

Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others.

• In this work, we generalize the framework of modularity maximization for clustering on hypergraphs.
• To this end, we introduce a hypergraph null model, analogous to the configuration model on undirected graphs, and a node-degree preserving reduction to work with this model.
• This is used to define a modularity function that can be maximized using the popular and fast Louvain algorithm.