NeurIPS 2021poster7 citations
Finding Bipartite Components in Hypergraphs
Abstract
Hypergraphs are important objects to model ternary or higher-order relations of objects, and have a number of applications in analysing many complex datasets occurring in practice. In this work we study a new heat diffusion process in hypergraphs, and employ this process to design a polynomial-time algorithm that approximately finds bipartite components in a hypergraph. We theoretically prove the performance of our proposed algorithm, and compare it against the previous state-of-the-art through extensive experimental analysis on both synthetic and real-world datasets. We find that our new algorithm consistently and significantly outperforms the previous state-of-the-art across a wide range of hypergraphs.
hypergraph clusteringspectral methods
BibTeX
@inproceedings{
macgregor2021finding,
title={Finding Bipartite Components in Hypergraphs},
author={Peter Macgregor and He Sun},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=fhDSTihtiB6}
}