dgl

Repository for benchmarking graph neural networks

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

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 (GNNs).

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

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

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

Visualization tool for Graph Neural Networks

An end-to-end solution 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.

Implementation of Directional Graph Networks in PyTorch and DGL

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

Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.

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

2021 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/

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

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

Senior Capstone Project: Graph-Based Product Recommendation

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning (PA-KDD 2021)

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

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

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

Multilabel Aspect Prediction using Graph Convolutional Networks

Various GNN implementation using DGL library

Predict CS Paper's Subject Area Using Graph Neural Networks

A DGL implementation of "Combining Label Propagation and Simple Models Out-performs Graph Neural Networks" (ICLR 2021).

8th place solution to https://www.automl.ai/competitions/3

DGL Implementations of P-GNN

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