StellarGraph - Machine Learning on Graphs

Benchmark datasets, data loaders, and evaluators for graph machine learning

Precision Medicine Knowledge Graph (PrimeKG)

Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)

[ACL 2022] LinkBERT: A Knowledgeable Language Model 😎 Pretrained with Document Links

Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric

A curated list of graph data augmentation papers.

A Python client for the Neo4j Graph Data Science (GDS) library

GraphXAI: Resource to support the development and evaluation of GNN explainers

Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)

OpenABC-D is a large-scale labeled dataset generated by synthesizing open source hardware IPs. This dataset can be used for various graph level prediction problems in chip design.

The integration of HugeGraph with AI/LLM & GraphRAG

Papers on Graph Analytics, Mining, and Learning

Implementation of Directional Graph Networks in PyTorch and DGL

[ECCV 2024] MSD: A Benchmark Dataset for Floor Plan of Building Complexes

SignNet and BasisNet

TigerLily: Finding drug interactions in silico with the Graph.

Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks

Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction

Given an input graph (ArangoDB or PyG) it generates graph embeddings using Low-Code framework built on top of PyG.