Alternatives and similar repositories for Awesome-Graph-Neural-Networks

Users that are interested in Awesome-Graph-Neural-Networks are comparing it to the libraries listed below

• graphdeeplearning / benchmarking-gnns
  Repository for benchmarking graph neural networks (JMLR 2023)
  ☆2,629Updated 2 years ago
• naganandy / graph-based-deep-learning-literature
  links to conference publications in graph-based deep learning
  ☆4,978Updated last week
• jdlc105 / Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress
  Papers on Graph neural network(GNN)
  ☆767Updated 2 years ago
• dsgiitr / graph_nets
  PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
  ☆1,215Updated 6 months ago
• weihua916 / powerful-gnns
  How Powerful are Graph Neural Networks?
  ☆1,249Updated 4 years ago
• DeepGraphLearning / LiteratureDL4Graph
  A comprehensive collection of recent papers on graph deep learning
  ☆3,083Updated 4 years ago
• tkipf / pygcn
  Graph Convolutional Networks in PyTorch
  ☆5,359Updated 5 years ago
• Jiakui / awesome-gcn
  resources for graph convolutional networks （图卷积神经网络相关资源）
  ☆902Updated 6 years ago
• benedekrozemberczki / CapsGNN
  A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).
  ☆1,268Updated 2 years ago
• ChandlerBang / awesome-self-supervised-gnn
  Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
  ☆1,687Updated last year
• snap-stanford / ogb
  Benchmark datasets, data loaders, and evaluators for graph machine learning
  ☆2,037Updated 5 months ago
• PetarV- / GAT
  Graph Attention Networks (https://arxiv.org/abs/1710.10903)
  ☆3,429Updated 3 years ago
• THUDM / CogDL
  CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
  ☆1,803Updated last year
• sungyongs / graph-based-nn
  Graph Convolutional Networks (GCNs)
  ☆919Updated 7 years ago
• Diego999 / pyGAT
  Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
  ☆3,069Updated 2 years ago
• microsoft / tf-gnn-samples
  TensorFlow implementations of Graph Neural Networks
  ☆927Updated 2 years ago
• lightaime / deep_gcns_torch
  Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
  ☆1,181Updated 3 years ago
• snap-stanford / GraphGym
  Platform for designing and evaluating Graph Neural Networks (GNN)
  ☆1,846Updated last year
• williamleif / graphsage-simple
  Simple reference implementation of GraphSAGE.
  ☆1,034Updated 5 years ago
• benedekrozemberczki / ClusterGCN
  A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
  ☆804Updated 2 years ago
• benedekrozemberczki / awesome-graph-classification
  A collection of important graph embedding, classification and representation learning papers with implementations.
  ☆4,794Updated 2 years ago
• RexYing / gnn-model-explainer
  gnn explainer
  ☆1,008Updated last year
• williamleif / GraphSAGE
  Representation learning on large graphs using stochastic graph convolutions.
  ☆3,600Updated last year
• mdeff / cnn_graph
  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
  ☆1,362Updated 5 years ago
• benedekrozemberczki / SimGNN
  A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
  ☆800Updated 2 years ago
• LYuhang / GNN_Review
  GNN综述阅读报告
  ☆918Updated 4 years ago
• seongjunyun / Graph_Transformer_Networks
  Graph Transformer Networks (Authors' PyTorch implementation for the NeurIPS 19 paper)
  ☆1,053Updated 2 years ago
• kimiyoung / planetoid
  Semi-supervised learning with graph embeddings
  ☆929Updated 5 years ago
• shenweichen / GraphNeuralNetwork
  Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.
  ☆834Updated 2 years ago
• FighterLYL / GraphNeuralNetwork
  《深入浅出图神经网络：GNN原理解析》配套代码
  ☆1,882Updated 4 years ago