hypergraph-partitioning

KaHyPar (Karlsruhe Hypergraph Partitioning) is a multilevel hypergraph partitioning framework providing direct k-way and recursive bisection based partitioning algorithms that compute solutions of very high quality.

Mt-KaHyPar (Multi-Threaded Karlsruhe Hypergraph Partitioner) is a shared-memory multilevel graph and hypergraph partitioner equipped with parallel implementations of techniques used in the best sequential partitioning algorithms. Mt-KaHyPar can partition extremely large hypergraphs very fast and with high quality.

FREIGHT: Fast Streaming Hypergraph Partitioning — SEA 2023 Best Paper Award

A list of all publications related to the KaHyPar frameworks.

Multiple-way hypergraph partitioning based on simulated annealing (Moved to Codeberg)

A non-official mt-kahypar Rust binding crate featuring fully static, self-contained build.

IP formulations for hypergraph partitioning problems

Hypergraph(or bipartite graph) clustering algorithm.

Python research simulations for silicon-photonic calibration: hypergraph partitioning, surrogate-based mesh optimization, and microring feedback control.

Hypergraph Harmony: Exploring Novel Community Detection through Hypergraph Embeddings and Hierarchical Analysis.

Python research code for silicon-photonic test-point selection using design-parameter risk weighting and KaHyPar partitioning, with example circuits, visualization, and overhead analysis.