Finding Bipartite Components in HypergraphsDownload PDF

21 May 2021, 20:50 (edited 25 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: hypergraph clustering, spectral methods
  • TL;DR: In this work we study a new heat diffusion process in hypergraphs, and design a polynomial-time algorithm that approximately finds bipartite components in a hypergraph.
  • 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.
  • Supplementary Material: pdf
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  • Code: https://github.com/pmacg/hypergraph-bipartite-components
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