graph-convolutional-networks
There are 377 repositories under graph-convolutional-networks topic.
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
benedekrozemberczki/awesome-graph-classification
A collection of important graph embedding, classification and representation learning papers with implementations.
naganandy/graph-based-deep-learning-literature
links to conference publications in graph-based deep learning
stellargraph/stellargraph
StellarGraph - Machine Learning on Graphs
alibaba/euler
A distributed graph deep learning framework.
benedekrozemberczki/pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
yao8839836/text_gcn
Graph Convolutional Networks for Text Classification. AAAI 2019
safe-graph/graph-fraud-detection-papers
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
lightaime/deep_gcns_torch
Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
dsgiitr/graph_nets
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
dair-ai/GNNs-Recipe
🟠 A study guide to learn about Graph Neural Networks (GNNs)
DSE-MSU/DeepRobust
A pytorch adversarial library for attack and defense methods on images and graphs
tsinghua-fib-lab/GNN-Recommender-Systems
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
IndexFziQ/GNN4NLP-Papers
A list of recent papers about Graph Neural Network methods applied in NLP areas.
shubhomoydas/ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
svjan5/GNNs-for-NLP
Tutorial: Graph Neural Networks for Natural Language Processing at EMNLP 2019 and CODS-COMAD 2020
benedekrozemberczki/ClusterGCN
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
yueliu1999/Awesome-Deep-Graph-Clustering
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
safe-graph/DGFraud
A Deep Graph-based Toolbox for Fraud Detection
Mariewelt/OpenChem
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
chainer/chainer-chemistry
Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry
PetarV-/DGI
Deep Graph Infomax (https://arxiv.org/abs/1809.10341)
malllabiisc/CompGCN
ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks
vaticle/typedb-ml
TypeDB-ML is the Machine Learning integrations library for TypeDB
chaitjo/efficient-gnns
Code and resources on scalable and efficient Graph Neural Networks
matenure/FastGCN
The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""
abduallahmohamed/Social-STGCNN
Code for "Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction" CVPR 2020
hwwang55/KGCN
A tensorflow implementation of Knowledge Graph Convolutional Networks
garyzhao/SemGCN
The Pytorch implementation for "Semantic Graph Convolutional Networks for 3D Human Pose Regression" (CVPR 2019).
mims-harvard/decagon
Graph convolutional neural network for multirelational link prediction
Cartus/AGGCN
Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)
zhiyongc/Graph_Convolutional_LSTM
Traffic Graph Convolutional Recurrent Neural Network
Zhongdao/gcn_clustering
Code for CVPR'19 paper Linkage-based Face Clustering via GCN
jwwthu/GNN-Communication-Networks
This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
kyzhouhzau/NLPGNN
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
bknyaz/graph_nn
Graph Classification with Graph Convolutional Networks in PyTorch (NeurIPS 2018 Workshop)