brandeis-machine-learning / FairAdjLinks
Code for ICLR'2021 paper: On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
☆12Updated 4 years ago
Alternatives and similar repositories for FairAdj
Users that are interested in FairAdj are comparing it to the libraries listed below
Sorting:
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆29Updated 3 years ago
- Paper List for Fair Graph Learning (FairGL).☆143Updated last year
- ☆14Updated 4 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆92Updated 4 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆116Updated 4 years ago
- ☆22Updated 3 years ago
- Ongoing project: a library for graph foundation model☆13Updated last year
- [ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"☆42Updated 3 years ago
- Implementation of "Bag of Tricks for Node Classification with Graph Neural Networks" based on DGL☆35Updated 11 months ago
- A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informati…☆70Updated 2 years ago
- Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph M…☆98Updated 2 years ago
- Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"☆59Updated 4 years ago
- Code for paper "Mixup for Node and Graph Classification", WWW 2021☆47Updated 4 years ago
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS…☆29Updated 3 years ago
- Pytorch implementation of gnn meta attack (mettack). Paper title: Adversarial Attacks on Graph Neural Networks via Meta Learning.☆21Updated 4 years ago
- ☆27Updated 3 years ago
- Open-source datasets for paper "Fairness in Graph Mining: A Survey".☆19Updated 3 years ago
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 5 years ago
- ☆18Updated 3 years ago
- How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications (KDD'22)☆13Updated 3 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆65Updated 2 years ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆105Updated last year
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆45Updated last year
- DP-GNN design that ensures both model weights and inference procedure differentially private (NeurIPS 2023)☆11Updated 2 years ago
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆81Updated 3 years ago
- source code of KDD 2022 paper "Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN".☆28Updated last year
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆47Updated 3 years ago
- This is the official repository for NeurIPS 2023 paper "Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First"☆17Updated 2 years ago
- A collection of graph data used for semi-supervised node classification.☆40Updated 3 years ago
- [ICLR'22][KDD'22][IJCAI'24][NeurIPS'25] Implementation of "Graph Condensation for Graph Neural Networks"☆141Updated 2 months ago