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Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy

Machine Learning 2023-07-14 v2

Abstract

We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergraph partitioning. By assigning vertex weights through the LPT algorithm, we generate a prior hypergraph under a relaxed balance constraint. With the prior hypergraph, we have defined the Wasserstein discrepancy to coordinate the optimal transport of coarsening process. And the optimal transport matrix is solved by Sinkhorn algorithm. Our coarsening scheme fully takes into account the minimization of connectivity metric (objective function). For the initial partitioning stage, we define a normalized cut function induced by Fiedler vector, which is theoretically proved to be a concave function. Thereby, a three-point algorithm is designed to find the best cut under the balance constraint.

Keywords

Cite

@article{arxiv.2106.07501,
  title  = {Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy},
  author = {Zhicheng Guo and Jiaxuan Zhao and Licheng Jiao and Xu Liu},
  journal= {arXiv preprint arXiv:2106.07501},
  year   = {2023}
}

Comments

There are some errors in the paper

R2 v1 2026-06-24T03:10:53.961Z