ispamm / FairDropLinks
☆14Updated 3 years ago
Alternatives and similar repositories for FairDrop
Users that are interested in FairDrop are comparing it to the libraries listed below
Sorting:
- Code for paper https://arxiv.org/abs/2102.13186☆43Updated 4 years ago
- Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)☆19Updated 2 years ago
- Paper List for Fair Graph Learning (FairGL).☆140Updated 9 months ago
- A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informati…☆67Updated last year
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆27Updated 3 years ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆106Updated last year
- A curated list of papers and code related to class-imbalanced learning on graphs (CILG).☆39Updated 5 months ago
- Code for ICLR'2021 paper: On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections☆12Updated 3 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- A repository contains a collection of resources and papers on Imbalance Learning On Graphs☆91Updated last month
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆43Updated 2 years ago
- Offical pytorch implementation of proposed NRGNN and Compared Methods in "NRGNN: Learning a Label Noise-Resistant Graph Neural Network on…☆42Updated 3 years ago
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆80Updated 3 years ago
- [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"☆140Updated 8 months ago
- An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))☆33Updated 3 years ago
- ☆57Updated 8 months ago
- ☆17Updated last month
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆169Updated last year
- [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…☆17Updated last year
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆123Updated 2 years ago
- Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms☆63Updated last year
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆90Updated 2 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆113Updated 3 years ago
- Parameterized Explainer for Graph Neural Network☆136Updated last year
- A curated list of graph data augmentation papers.☆311Updated last year
- Source code of NeurIPS 2022 paper “Co-Modality Graph Contrastive Learning for Imbalanced Node Classification”☆20Updated 2 years ago
- ☆55Updated 3 years ago
- General Strategy for Unlearning in Graph Neural Networks☆44Updated 2 years ago
- Papers about out-of-distribution generalization on graphs.☆165Updated 2 years ago
- code implementation of SEP(ICML 2022)☆34Updated 2 years ago