brandeis-machine-learning / FairAdj
Code for ICLR'2021 paper: On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
☆12Updated 3 years ago
Alternatives and similar repositories for FairAdj:
Users that are interested in FairAdj are comparing it to the libraries listed below
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆26Updated 2 years ago
- ☆14Updated 3 years ago
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 4 years ago
- Open-source datasets for paper "Fairness in Graph Mining: A Survey".☆18Updated 2 years ago
- ☆18Updated 3 years ago
- ☆26Updated this week
- [ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"☆42Updated 2 years ago
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).☆30Updated last year
- Defending graph neural networks against adversarial attacks (NeurIPS 2020)☆64Updated last year
- Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"☆58Updated 3 years ago
- ☆25Updated 2 years ago
- source code of KDD 2022 paper "Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN".☆28Updated 10 months ago
- G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)☆30Updated 3 years ago
- A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informati…☆64Updated last year
- [WSDM 2023] "Alleviating Structrual Distribution Shift in Graph Anomaly Detection" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, H…☆20Updated last year
- ☆24Updated 2 years ago
- Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms☆62Updated 11 months ago
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS…☆29Updated 2 years ago
- source code of KDD 2021 paper "Pre-training on Large-Scale Heterogeneous Graph".☆22Updated 3 years ago
- ☆20Updated 2 years ago
- [ICLR 2023] "Graph Domain Adaptation via Theory-Grounded Spectral Regularization" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆21Updated 2 years ago
- [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…☆17Updated last year
- Ongoing project: a library for graph foundation model☆13Updated last year
- An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))☆33Updated 2 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆56Updated last year
- DP-GNN design that ensures both model weights and inference procedure differentially private (NeurIPS 2023)☆12Updated last year
- This is the official repository for NeurIPS 2023 paper "Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First"☆15Updated last year
- ☆15Updated 2 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction☆66Updated 2 years ago