LFhase / CIGALinks
[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
☆116Updated 2 years ago
Alternatives and similar repositories for CIGA
Users that are interested in CIGA are comparing it to the libraries listed below
Sorting:
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆92Updated 2 years ago
- NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure☆41Updated last year
- GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]☆202Updated 7 months ago
- (ICLR 2022) Discovering Invariant Rationales for Graph Neural Networks☆131Updated 2 years ago
- [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…☆94Updated last year
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆168Updated last year
- Ratioanle-aware Graph Contrastive Learning codebase☆43Updated 2 years ago
- Codes for 'From Canonical Correlation Analysis to Self-supervised Graph Neural Networks'. https://arxiv.org/abs/2106.12484☆69Updated last year
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆53Updated 2 years ago
- [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"☆141Updated 11 months ago
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆45Updated last year
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆61Updated 2 years ago
- ☆56Updated 3 years ago
- The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"☆82Updated 2 years ago
- [ICML 2022] Local Augmentation for Graph Neural Networks☆65Updated last year
- [NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?☆22Updated last year
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆105Updated last year
- [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)☆21Updated 2 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆90Updated 3 years ago
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆81Updated 3 years ago
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", wh…☆90Updated last year
- Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"☆81Updated 3 years ago
- Papers about out-of-distribution generalization on graphs.☆167Updated 2 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆137Updated 2 years ago
- [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection☆44Updated 2 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 3 years ago
- A collection of papers on Graph Structural Learning (GSL)☆56Updated last year
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆82Updated 2 years ago
- Official repository for ICLR'23 paper: Multi-task Self-supervised Graph Neural Network Enable Stronger Task Generalization☆39Updated 2 years ago
- Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods☆70Updated 7 months ago