Repository for benchmarking graph neural networks

High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.

Protein Graph Library

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.

Python package for graph neural networks in chemistry and biology

A knowledge graph and a set of tools for drug repurposing

GraphGallery is a gallery for benchmarking Graph Neural Networks, From InplusLab.

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

Bag of Tricks for Graph Neural Networks.

Visualization tool for Graph Neural Networks

An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.

Source code for EMNLP 2020 paper: Double Graph Based Reasoning for Document-level Relation Extraction

Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.

Implementation of Directional Graph Networks in PyTorch and DGL

DGL中文文档。This is the Chinese manual of the graph neural network library DGL, currently contains the User Guide.

Code for "Heterogeneous Graph Transformer" (WWW'20), which is based on Deep Graph Library (DGL)

Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.

PyTorch-Direct code on top of PyTorch-1.8.0nightly (e152ca5) for Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture (accepted by PVLDB)

Senior Capstone Project: Graph-Based Product Recommendation

2021MXAP-DGL rank2