Hypergraph Random Walks, Laplacians, and Clustering
Abstract
We propose a flexible framework for clustering hypergraph-structured data based on recently proposed random walks utilizing edge-dependent vertex weights. When incorporating edge-dependent vertex weights (EDVW), a weight is associated with each vertex-hyperedge pair, yielding a weighted incidence matrix of the hypergraph. Such weightings have been utilized in term-document representations of text data sets. We explain how random walks with EDVW serve to construct different hypergraph Laplacian matrices, and then develop a suite of clustering methods that use these incidence matrices and Laplacians for hypergraph clustering. Using several data sets from real-life applications, we compare the performance of these clustering algorithms experimentally against a variety of existing hypergraph clustering methods. We show that the proposed methods produce higher-quality clusters and conclude by highlighting avenues for future work.
Cite
@article{arxiv.2006.16377,
title = {Hypergraph Random Walks, Laplacians, and Clustering},
author = {Koby Hayashi and Sinan G. Aksoy and Cheong Hee Park and Haesun Park},
journal= {arXiv preprint arXiv:2006.16377},
year = {2020}
}