dgl

awslabs/dgl-ke

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

a-r-j/graphein

Protein Graph Library

BUPT-GAMMA/OpenHGNN

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

awslabs/dgl-lifesci

Python package for graph neural networks in chemistry and biology

gnn4dr/DRKG

A knowledge graph and a set of tools for drug repurposing

EdisonLeeeee/GraphGallery

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

lukecavabarrett/pna

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

sangyx/gtrick

Bag of Tricks for Graph Neural Networks.

dmlc/GNNLens2

Visualization tool for Graph Neural Networks

awslabs/realtime-fraud-detection-with-gnn-on-dgl

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.

IntelLabs/matsciml

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.

DreamInvoker/GAIN

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

Saro00/DGN

Implementation of Directional Graph Networks in PyTorch and DGL

taishan1994/DGL_Chinese_Manual

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

acbull/HGT-DGL

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

shionhonda/gae-dgl

Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.

K-Wu/pytorch-direct_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)

nhtsai/graph-rec

Senior Capstone Project: Graph-Based Product Recommendation

waittim/graph-fraud-detection

Colab implementation for Fraud Detection in Graph Neural Networks, based on Deep Graph Library (DGL) and PyTorch backend.

wey-gu/nebula-dgl

NebulaGraph DGL(Deep Graph Library) Integration Package. (WIP)

vinayakakv/android-malware-detection

Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.

DiffEqML/pde-dataset-generator

A tool for generating PDEs ground truth datasets from ARCSim, FEniCS and SU2

langgege-cqu/maxp_dgl

2021MXAP-DGL rank2

cmavro/Graph-InfoClust-GIC

[PAKDD 2021] Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

wey-gu/NebulaGraph-Fraud-Detection-GNN

An example project for training a GraphSAGE Model, and setup a Real-time Fraud Detection Web Service(Frontend and Backend) with NebulaGraph Database and DGL.

ceo21ckim/DGL-Tutorial

This Repository includes DGL tutorials and various information related to graph neural networks.

ytchx1999/MAXP_DGL_Graph

MAXP 命题赛 任务一：基于DGL的图机器学习任务。队伍：Graph@ICT，🥉rank6。https://www.biendata.xyz/competition/maxp_dgl/

BUPT-GAMMA/Space4HGNN

Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network

itlab-vision/dl-benchmark

Deep Learning Inference benchmark. Supports OpenVINO™ toolkit, TensorFlow, TensorFlow Lite, ONNX Runtime, OpenCV DNN, MXNet, PyTorch, Apache TVM, ncnn, PaddlePaddle, etc.

Wang-Yu-Qing/EGES

DGL implementation of EGES

sxwee/GNNsIMPL

JetBrains-Research/embeddings-for-trees

Set of PyTorch modules for developing and evaluating different algorithms for embedding trees.

xnuohz/DimeNet-dgl

A DGL implementation of "Directional Message Passing for Molecular Graphs" (ICLR 2020).

xnuohz/ARMA-dgl

A DGL implementation of "Graph Neural Networks with convolutional ARMA filters". (PAMI 2021)

zahta/graph_ml

Course: Graph Machine Learning focuses on the application of machine learning algorithms on graph-structured data. Some of the key topics that are covered in the course include graph representation learning and graph neural networks, algorithms for the world wide web, reasoning over knowledge graphs, and social network analysis.