Top 38 graph-convolutional-networks open source projects

A distributed graph deep learning framework.

StellarGraph - Machine Learning on Graphs

Graph Neural Network Library for PyTorch

A collection of important graph embedding, classification and representation learning papers with implementations.

links to conference publications in graph-based deep learning

Summary of open source code for deep learning models in the field of traffic prediction

PyTorch Implementation of GraphTSNE, ICLR’19

Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders (IJCAI 2018)

Code for Graph Convolutional Matrix Factorization for Bipartite Edge Prediction

ProteinGCN: Protein model quality assessment using Graph Convolutional Networks

Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper

A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

Tensorflow Implementation of the paper "Topology Adaptive Graph Convolutional Networks" (Du et al., 2017)

The implement of GNN based on Pytorch

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

A reading list for deep graph learning acceleration.

A graph-based deep learning framework for life science

ACL 2018: Dating Documents using Graph Convolution Networks

Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

PyTorch implementation of our paper "Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing" (NAACL19)

Code for the BMVC paper (http://bmvc2018.org/contents/papers/1003.pdf)

Representation Learning on Graphs with Jumping Knowledge Networks

TypeDB-ML is the Machine Learning integrations library for TypeDB

⚡ Using deep learning (MLP, CNN, Graph CNN) to classify text in TensorFlow.

A list of data mining and machine learning papers that I implemented in 2019.

Tutorials for Machine Learning on Graphs

Official repository for the paper "Learning Local Neighboring Structure for Robust 3D Shape Representation"

This project is a collection of recent research in areas such as new infrastructure and urban computing, including white papers, academic papers, AI lab and dataset etc.

Chainer implementation of 'Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering' (https://arxiv.org/abs/1606.09375)

An index of recommendation algorithms that are based on Graph Neural Networks.

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning (authors' MXNet implementation for the TACL19 paper)

A tutorial & minimal example (8min on CPU) for Graph Convolutional Networks for Text Classification. AAAI 2019

Code and resources on scalable and efficient Graph Neural Networks

A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in The International Workshop on Self-Supervised Learning for the Web (SSL'21) @ the Web Conference 2021 (WWW'21).

codes of Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction

[CVPR 2020] L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks

Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation, AAAI 2020