isail-laboratory / iDEA-iSAIL-Reading-Group
☆26Updated last week
Alternatives and similar repositories for iDEA-iSAIL-Reading-Group:
Users that are interested in iDEA-iSAIL-Reading-Group are comparing it to the libraries listed below
- ☆2Updated 3 years ago
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆26Updated 2 years ago
- A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informati…☆60Updated last year
- ☆18Updated 2 years ago
- Paper List for Fair Graph Learning (FairGL).☆134Updated 4 months ago
- Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms☆62Updated 8 months ago
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 3 years ago
- source code of KDD 2022 paper "Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN".☆27Updated 8 months ago
- An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))☆32Updated 2 years ago
- Comprehensive Benchmark Dataset for Dynamic Text-Attributed Graphs☆28Updated 2 months ago
- Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)☆19Updated last year
- Open-source datasets for paper "Fairness in Graph Mining: A Survey".☆17Updated 2 years ago
- ☆52Updated 2 months ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆84Updated 2 years ago
- The repository of "Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark" (KDD'24)☆10Updated 3 months ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆96Updated 11 months ago
- Pytorch implementation of gnn meta attack (mettack). Paper title: Adversarial Attacks on Graph Neural Networks via Meta Learning.☆21Updated 3 years ago
- Offical pytorch implementation of proposed NRGNN and Compared Methods in "NRGNN: Learning a Label Noise-Resistant Graph Neural Network on…☆42Updated 2 years ago
- This is the implementation of OODGAT from KDD'22: Learning on Graphs with Out-of-Distribution Nodes.☆22Updated 2 years ago
- ☆50Updated 4 months ago
- ☆24Updated 2 years ago
- ☆25Updated 2 years ago
- [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…☆16Updated last year
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS…☆29Updated 2 years ago
- [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)☆21Updated last year
- ☆22Updated 2 years ago
- G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)☆28Updated 3 years ago
- How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications (KDD'22)☆12Updated 2 years ago
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆42Updated 2 years ago
- [WSDM 2023] "Alleviating Structrual Distribution Shift in Graph Anomaly Detection" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, H…☆20Updated last year