Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning
This addresses the need for simultaneous clustering of data entities and features in applications like data analysis, though it is incremental as it builds on existing hypergraph models with added expressivity.
The paper tackles the problem of co-clustering vertices and hyperedges in hypergraphs with edge-dependent vertex weights (EDVWs), proposing a spectral partitioning method that embeds both in a common space, and demonstrates effectiveness in real-world data experiments compared to state-of-the-art alternatives.
We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs). In this hypergraph model, the contribution of every vertex to each of its incident hyperedges is represented through an edge-dependent weight, conferring the model higher expressivity than the classical hypergraph. In our method, we leverage random walks with EDVWs to construct a hypergraph Laplacian and use its spectral properties to embed vertices and hyperedges in a common space. We then cluster these embeddings to obtain our proposed co-clustering method, of particular relevance in applications requiring the simultaneous clustering of data entities and features. Numerical experiments using real-world data demonstrate the effectiveness of our proposed approach in comparison with state-of-the-art alternatives.