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.

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

Papers on Graph Analytics, Mining, and Learning

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.

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)

Parallel graph partitioning

A random graph partitioning algorithm inspired from label propagation method

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

The algorithm based on the UBQP model (Aref et al. 2018) for computing the exact value of frustration index (also called line index of balance)

The algorithms for multilevel evaluation of balance in signed directed networks

This is the source code of the algorithm described in the paper: "On Using Graph Partitioning with Isomorphism Constraint in Procedural Content Generation" presented at PCG Workshop 2017 part of FDG 2017.

A list of all publications related to the KaHyPar frameworks.

Solve graph partitioning problem experimenting IP, CP (ortools) , Tabu search and GA

Judicious Graph Partitioning