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.
Cite
Text
Macgregor and Sun. "Finding Bipartite Components in Hypergraphs." Neural Information Processing Systems, 2021.Markdown
[Macgregor and Sun. "Finding Bipartite Components in Hypergraphs." Neural Information Processing Systems, 2021.](https://mlanthology.org/neurips/2021/macgregor2021neurips-finding/)BibTeX
@inproceedings{macgregor2021neurips-finding,
title = {{Finding Bipartite Components in Hypergraphs}},
author = {Macgregor, Peter and Sun, He},
booktitle = {Neural Information Processing Systems},
year = {2021},
url = {https://mlanthology.org/neurips/2021/macgregor2021neurips-finding/}
}