Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy
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Abstract (arXiv)
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.
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References · 13
- 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
- 2019Gromov-Wasserstein Learning for Graph Matching and Node Embeddingcited by 125
- 2019Wasserstein Weisfeiler-Lehman Graph Kernelscited by 104
- 2019Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matchingcited by 102
- 2020Generalized Spectral Clustering via Gromov-Wasserstein Learningcited by 37
- 2020Optimal Transport Graph Neural Networkscited by 24
- 2015k-way Hypergraph Partitioning via n-Level Recursive Bisectioncited by 7
- 2017Memetic Multilevel Hypergraph Partitioningcited by 4
- 2018Network Flow-Based Refinement for Multilevel Hypergraph Partitioningcited by 3
- 2020Advanced Flow-Based Multilevel Hypergraph Partitioningcited by 3
- 2014Partitioning Complex Networks via Size-constrained Clusteringcited by 2
- 2021Multilevel Hypergraph Partitioning with Vertex Weights Revisitedcited by 2
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