hypergraph partitioning
6 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in hypergraph partitioning
Latest papers
HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering
This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances.
HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering
To address the ever-increasing computational challenges, graph coarsening can be potentially applied for preprocessing a given hypergraph by aggressively aggregating its vertices (nodes).
Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning
We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs).
Inhomogeneous Hypergraph Clustering with Applications
Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics.
Improving Coarsening Schemes for Hypergraph Partitioning by Exploiting Community Structure
We present an improved coarsening process for multilevel hypergraph partitioning that incorporates global information about the community structure.
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions
We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution.
