Alternatives and similar repositories for GSAT

Users that are interested in GSAT are comparing it to the libraries listed below

• divelab / GOOD
  GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
  ☆196Updated 3 months ago
• LFhase / CIGA
  [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
  ☆113Updated last year
• qitianwu / GraphOOD-EERM
  The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
  ☆89Updated 2 years ago
• THUMNLab / awesome-graph-ood
  Papers about out-of-distribution generalization on graphs.
  ☆167Updated last year
• flyingdoog / PGExplainer
  Parameterized Explainer for Graph Neural Network
  ☆132Updated last year
• GemsLab / H2GCN
  Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
  ☆103Updated 3 years ago
• LarsHoldijk / RE-ParameterizedExplainerForGraphNeuralNetworks
  ☆55Updated 3 years ago
• SongtaoLiu0823 / LAGNN
  [ICML 2022] Local Augmentation for Graph Neural Networks
  ☆65Updated 11 months ago
• ahxt / g-mixup
  [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification
  ☆103Updated last year
• susheels / adgcl
  Adversarial Graph Augmentation to Improve Graph Contrastive Learning
  ☆88Updated 3 years ago
• Shen-Lab / GraphCL_Automated
  [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…
  ☆113Updated 8 months ago
• junxia97 / SimGRACE
  [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
  ☆80Updated 2 years ago
• zaixizhang / ProtGNN
  Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"
  ☆52Updated 2 years ago
• zepengzhang / awesome-graph-structure-learning
  A collection of papers on Graph Structural Learning (GSL)
  ☆54Updated last year
• nerdslab / bgrl
  PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)
  ☆81Updated last year
• Namkyeong / BGRL_Pytorch
  Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"
  ☆80Updated 3 years ago
• logseminar / Schedule
  Schedule for learning on graphs seminar
  ☆109Updated last year
• SitaoLuan / ACM-GNN
  NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…
  ☆81Updated 6 months ago
• yandex-research / heterophilous-graphs
  A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?
  ☆116Updated 2 years ago
• chirag-agarwall / nifty
  Code for paper https://arxiv.org/abs/2102.13186
  ☆44Updated 4 years ago
• CUAI / Non-Homophily-Large-Scale
  [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
  ☆122Updated 2 years ago
• LingxiaoShawn / GNNAsKernel
  Source code for From Stars to Subgraphs (ICLR 2022)
  ☆70Updated last year
• wanyu-lin / ICML2021-Gem
  Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
  ☆66Updated 3 years ago
• JHL-HUST / NAGphormer
  NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
  ☆125Updated last year
• EdisonLeeeee / GreatX
  A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
  ☆85Updated 7 months ago
• jianhao2016 / GPRGNN
  ☆135Updated last year
• MingChen-Sun / GPPT
  ☆61Updated 2 years ago
• ivam-he / BernNet
  PyTorch implementation of "BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation"
  ☆54Updated 2 years ago
• yongduosui / CAL
  [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…
  ☆90Updated last year
• vunhatminh / PGMExplainer
  Generating PGM Explanation for GNN predictions
  ☆75Updated last year