  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.
