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/}
}