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.

KaHIP -- Karlsruhe HIGH Quality Partitioning.

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.

An implementation of "EdMot: An Edge Enhancement Approach for Motif-aware Community Detection" (KDD 2019)

Papers on Graph Analytics, Mining, and Learning

A NetworkX implementation of Label Propagation from a "Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks" (Physical Review E 2008).

Implementation of Kernighan-Lin graph partitioning algorithm in Python

Graph edge partitioning algorithms

A modern Fortran interface to the METIS graph partitioning library

DRL models for graph partitioning and sparse matrix ordering.

Implements a generalized Louvain algorithm (C++ backend and Matlab interface)

A list of all publications related to the KaHyPar frameworks.

Parallel graph partitioning

A random graph partitioning algorithm inspired from label propagation method

A GPT-GNN based verilog netlist partitioner for 3D IC design

The algorithms for multilevel evaluation of balance in signed directed networks

Must-read papers on streaming graph

Implementation of the expander decomposition algorithm in https://arxiv.org/abs/1812.08958. Decompose graph with cluster expansion guarantee.

USENIX Security'23: Inductive Graph Unlearning

CutESC: Cutting Edge Spatial Clustering Technique based on Proximity Graphs