Graph-COM / GSATLinks
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
☆170Updated last year
Alternatives and similar repositories for GSAT
Users that are interested in GSAT are comparing it to the libraries listed below
Sorting:
- GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]☆202Updated 8 months ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆116Updated 2 years ago
- [ICML 2022] Local Augmentation for Graph Neural Networks☆65Updated last year
- Papers about out-of-distribution generalization on graphs.☆167Updated 2 years ago
- Parameterized Explainer for Graph Neural Network☆139Updated last year
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆52Updated 3 years ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆105Updated last year
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆106Updated 4 months ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆137Updated 2 years ago
- A collection of papers on Graph Structural Learning (GSL)☆56Updated last year
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆92Updated 2 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆90Updated 3 years ago
- [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…☆114Updated last year
- Code for paper https://arxiv.org/abs/2102.13186☆43Updated 4 years ago
- Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods☆70Updated 8 months ago
- [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…☆96Updated last year
- ☆55Updated 3 years ago
- [ICLR'22][KDD'22][IJCAI'24][NeurIPS'25] Implementation of "Graph Condensation for Graph Neural Networks"☆142Updated 2 weeks ago
- ☆138Updated 2 years ago
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆81Updated 3 years ago
- Ratioanle-aware Graph Contrastive Learning codebase☆43Updated 2 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆116Updated 3 years ago
- NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…☆86Updated 11 months ago
- [KDD 2023] What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders☆88Updated 11 months ago
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆125Updated 3 years ago
- How Powerful are Spectral Graph Neural Networks☆73Updated 2 years ago
- Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminat…☆56Updated 2 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆89Updated 4 years ago
- A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?☆120Updated 2 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆82Updated 2 years ago