Popular GitHub Repositories for Dgl Ke

Repository for benchmarking graph neural networks (JMLR 2023)

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

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

Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

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.

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

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

Graph Convolutional Networks (GCN) with BERT for Coreference Resolution Task [Pytorch][DGL]

Implementation of Directional Graph Networks in PyTorch and DGL

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

Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.

KE-Dialogue: Injecting knowledge graph into a fully end-to-end dialogue system.

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

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