5 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in hypergraph partitioning
We present an improved coarsening process for multilevel hypergraph partitioning that incorporates global information about the community structure.
We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs).
To address the ever-increasing computational challenges, graph coarsening can be potentially applied for preprocessing a given hypergraph by aggressively aggregating its vertices (nodes).
This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances.