Graph-COM / GSATLinks
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
☆168Updated 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]☆201Updated 6 months ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆116Updated last year
- [ICML 2022] Local Augmentation for Graph Neural Networks☆65Updated last year
- Parameterized Explainer for Graph Neural Network☆138Updated last year
- [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…☆113Updated 11 months ago
- Papers about out-of-distribution generalization on graphs.☆165Updated 2 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆89Updated 3 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆137Updated 2 years ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆106Updated last year
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆81Updated 3 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆104Updated 2 months ago
- [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…☆92Updated last year
- [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"☆140Updated 9 months ago
- ☆55Updated 2 years ago
- ☆55Updated 3 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆114Updated 3 years ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆92Updated 2 years ago
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆53Updated 2 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆81Updated last year
- A collection of papers on Graph Structural Learning (GSL)☆55Updated last year
- Code for paper https://arxiv.org/abs/2102.13186☆43Updated 4 years ago
- Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminat…☆55Updated 2 years ago
- Ratioanle-aware Graph Contrastive Learning codebase☆44Updated 2 years ago
- [KDD 2023] What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders☆88Updated 9 months ago
- NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…☆85Updated 9 months ago
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", wh…☆89Updated last year
- A repository contains a collection of resources and papers on Imbalance Learning On Graphs☆92Updated 2 months ago
- ☆56Updated 11 months ago
- Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods☆69Updated 6 months ago
- Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"☆81Updated 3 years ago