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28 search results found

Benchmarking Gnns ⭐ 2,196

Repository for benchmarking graph neural networks

Dgl Ke ⭐ 1,091

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

Graphein ⭐ 932

Protein Graph Library

Openhgnn ⭐ 737

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

Dgl Lifesci ⭐ 558

Python package for graph neural networks in chemistry and biology

Graphgallery ⭐ 440

GraphGallery is a gallery for benchmarking Graph Neural Networks.

A knowledge graph and a set of tools for drug repurposing

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

Bag of Tricks for Graph Neural Networks.

Realtime Fraud Detection With Gnn On Dgl ⭐ 191

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.

Gnnlens2 ⭐ 136

Visualization tool for Graph Neural Networks

Matsciml ⭐ 94

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.

Dgl_chinese_manual ⭐ 49

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)

Pytorch Direct_dgl ⭐ 32

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)

Nebula Dgl ⭐ 30

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

Dgl Tutorial ⭐ 27

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

Implementation of Directional Graph Networks in PyTorch and DGL

Maxp_dgl_graph ⭐ 24

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

Embeddings For Trees ⭐ 17

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

DGL implementation of EGES

Graph Infoclust Gic ⭐ 13

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

Dimenet Dgl ⭐ 13

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

Graph_ml ⭐ 10

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.

Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.

Denoise Pretrain Ml Potential ⭐ 7

Implementation of "Denoise Pretraining on Non-equilibrium Molecular Conformations for Accurate and Transferable Neural Potentials" in PyTorch.

DGL implementation of GNN-CCA: Graph Neural Networks for Cross-Camera Data Association [arXiv:2201.06311]

Learnable_dropedge ⭐ 5

Multilabel Aspect Prediction using Graph Convolutional Networks

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