dgl
There are 2 repositories under dgl topic.
graphdeeplearning / benchmarking-gnns
Repository for benchmarking graph neural networks
Language:Jupyter Notebook 2434awslabs / dgl-ke
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
Language:Python 1242- grapheinLanguage:Jupyter Notebook 989
BUPT-GAMMA / OpenHGNN
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.
Language:Python 794Python package for graph neural networks in chemistry and biology
Language:Python 689- DRKGLanguage:Jupyter Notebook 557
EdisonLeeeee / GraphGallery
GraphGallery is a gallery for benchmarking Graph Neural Networks, From InplusLab.
Language:Python 455lukecavabarrett / pna
Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric
Language:Python 329- Language:Jupyter Notebook 272
- Language:TypeScript 213
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.
Language:TypeScript 205DreamInvoker / GAIN
Source code for EMNLP 2020 paper: Double Graph Based Reasoning for Document-level Relation Extraction
Language:Python 141IntelLabs / 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.
Language:Python 127- Language:Python 111
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)
Language:Python 65shionhonda / gae-dgl
Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.
Language:Python 65K-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)
- Language:Jupyter Notebook 38
- Language:Python 36
wey-gu / nebula-dgl
NebulaGraph DGL(Deep Graph Library) Integration Package. (WIP)
Language:Python 35cmavro / Graph-InfoClust-GIC
[PAKDD 2021] Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
Language:Jupyter Notebook 34- pde-dataset-generator
DiffEqML / pde-dataset-generator
A tool for generating PDEs ground truth datasets from ARCSim, FEniCS and SU2
Language:Jupyter Notebook 33 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.
Language:Jupyter Notebook 31ceo21ckim / DGL-Tutorial
This Repository includes DGL tutorials and various information related to graph neural networks.
Language:Jupyter Notebook 29vinayakakv / android-malware-detection
Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.
Language:Jupyter Notebook 29ytchx1999 / 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/
Language:Python 29Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Language:Python 26itlab-vision / dl-benchmark
Deep Learning Inference benchmark. Supports OpenVINO™ toolkit, Caffe, TensorFlow, TensorFlow Lite, ONNX Runtime, OpenCV DNN, MXNet, PyTorch, Apache TVM, ncnn, etc.
Language:HTML 24waittim / graph-fraud-detection
Colab implementation for Fraud Detection in Graph Neural Networks, based on Deep Graph Library (DGL) and PyTorch backend.
Language:Jupyter Notebook 23JetBrains-Research / embeddings-for-trees
Set of PyTorch modules for developing and evaluating different algorithms for embedding trees.
Language:Python 22Wang-Yu-Qing / EGES
DGL implementation of EGES
Language:Python 22- Language:Python 19
xnuohz / DimeNet-dgl
A DGL implementation of "Directional Message Passing for Molecular Graphs" (ICLR 2020).
Language:Python 18A DGL implementation of "Graph Neural Networks with convolutional ARMA filters". (PAMI 2021)
Language:Python 15zahta / 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.
Language:Jupyter Notebook 13
