KaHIP -- Karlsruhe HIGH Quality 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.

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

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)

Implementation of Kernighan-Lin graph partitioning algorithm in Python

Papers on Graph Analytics, Mining, and Learning

Graph edge partitioning algorithms

A modern Fortran interface to the METIS graph partitioning library

DRL models for graph partitioning and sparse matrix ordering.

CutESC: Cutting Edge Spatial Clustering Technique based on Proximity Graphs

A list of all publications related to the KaHyPar frameworks.

The Kernighan–Lin algorithm is a heuristic algorithm for finding partitions of graphs. The algorithm has important applications in the layout of digital circuits and components in VLSI.

A command-line tool for simple, single-step retrosynthetic reaction prediction using graph partitioning.

Graph Partitioning using the JA-BE-JA algorithm

Source code for VLDB2024 - FSM: A Fine-grained Splitting and Merging Framework for Dual-balanced Graph Partition

Extraction of voting networks

A graph partitioning algorithm for spatial network for parallel and distributed computing

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