The eigenvector centrality of hypergraphs
This work extends eigenvector centrality to non-uniform hypergraphs, offering a new tool for network analysis in domains like email and co-authorship networks.
The authors define an adjacency tensor for non-uniform hypergraphs and propose an eigenvector centrality measure that generalizes Benson's method for uniform hypergraphs. Experiments on real-world datasets show the measure provides a unique perspective for identifying important vertices.
A hypergraph is called uniform when every hyperedge contains the same number of vertices, otherwise, it is called non-uniform. In the real world, many systems give rise to non-uniform hypergraphs, such as email networks and co-authorship networks. A uniform hypergraph has a natural one-to-one correspondence with its adjacency tensor. In 2019, Benson proposed the eigenvector centrality of uniform hypergraphs via its adjacency tensor. In this paper, we define an adjacency tensor for hypergraphs and propose the eigenvector centrality for hypergraphs. When the hypergraph is uniform, our proposed eigenvector centrality reduces to Benson's. When each edge of the uniform hypergraph contains exactly two vertices, our proposed centrality reduces to the eigenvector centrality of graphs. We conducted experiments on several real-world hypergraph datasets. The results show that, compared to traditional centrality measures, the proposed centrality measure provides a unique perspective for identifying important vertices and can also effectively identify them.