@inproceedings{f4cd0eca84d44cec9f243c802d9d155a,
title = "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.",
author = "Peter Macgregor and He Sun",
note = "Funding: This work is supported by a Langmuir PhD Scholarship, and an EPSRC Early Career Fellowship (EP/T00729X/1).",
year = "2021",
month = dec,
day = "14",
language = "English",
isbn = "9781713845393",
series = "Advances in neural information processing systems",
publisher = "Neural Information Processing Systems Foundation, Inc. (NeurIPS)",
pages = "7912--7923",
editor = "M. Ranzato and A. Beygelzimer and Y. Dauphin and Liang, \{P. S.\} and \{Wortman Vaughan\}, J.",
booktitle = "Advances in neural information processing systems 34",
}