Hypergraph Neural Networks (AAAI 2019)

Python package for hypergraph analysis and visualization.

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.

Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning

EasyGraph is an open-source network analysis library designed to cover advanced network processing methods. It includes functionalities for detecting structural hole spanners, network embedding, and various classic network analysis techniques.

[NeurIPS 2025] Official resources of "HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation".

[NeurIPS'22] Tokenized Graph Transformer (TokenGT), in PyTorch

Hypergraph is data structure library to create a directed hypergraph in which a hyperedge can join any number of vertices.

A curated list of Hypergraph Learning, Hypergraph Theory, Hypergraph Dataset and Hypergraph Tool.

C++/Wolfram Language package for exploring set and graph rewriting systems

NeurIPS 2019: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

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.

The source code of IEEE TPAMI 2025 "Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation".

Implementation of EMNLP2020 -- Be More with Less: Hypergraph Attention Networks for Inductive Text Classification

HypergraphZ - A Hypergraph Implementation in Zig

Hypergraph Rewriting System

single-cell Hi-C, scHi-C, Hi-C, 3D genome, nuclear organization, hypergraph

A performant, parallel, probabilistic, random acyclic-graph, low-latency, perfect hash generation library.

Collection of papers relating data-driven higher-order graph/networks researches.

[NeurIPS'21] Higher-order Transformers for sets, graphs, and hypergraphs, in PyTorch

Hypergraph Database with Hypergraph Visualization.

Code of the paper "Game theoretic hypergraph matching for multi-source image correspondences". [论文代码] 超图匹配和多源图像特征点匹配。

[WWW'21] Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks

Code for Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting

A simplicial complex and hypergraph visualization tool similar to Graphviz.

[NeurIPS 2024] Official resources of "Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction".

The source code of ICLR 2025 "Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?"

The jBPT code library is a compendium of technologies that support research on design, execution, and evaluation of business processes. The library offers a broad range of basis analysis and utility functionality and, due to its open publishing model, can easily be extended.

Chapel HyperGraph Library (CHGL) - HPC-class Hypergraphs in Chapel

[ECCV'22] Equivariant Hypergraph Neural Networks, in PyTorch