Mark12Ding / GNN-Practical-Attack
Code for Towards More Practical Adversarial Attacks on Graph Neural Networks (NeurIPS 2020)
☆26Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for GNN-Practical-Attack
- [ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability☆37Updated 11 months ago
- G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)☆26Updated 2 years ago
- Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem☆18Updated 3 years ago
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).☆28Updated last year
- code for paper TDGIA:Effective Injection Attacks on Graph Neural Networks (KDD 2021, research track)☆18Updated 3 years ago
- Pytorch implementation of gnn meta attack (mettack). Paper title: Adversarial Attacks on Graph Neural Networks via Meta Learning.☆20Updated 3 years ago
- Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS…☆28Updated 2 years ago
- ☆21Updated 2 years ago
- Official repository for AAAI'23 paper: Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinf…☆21Updated last year
- Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, WWW22☆17Updated 2 years ago
- source code of KDD 2022 paper "Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN".☆27Updated 5 months ago
- A PyTorch implementation of "Backdoor Attacks to Graph Neural Networks" (SACMAT'21)☆34Updated 3 years ago
- [WWW2022] Geometric Graph Representation Learning via Maximizing Rate Reduction☆26Updated 2 years ago
- Defending graph neural networks against adversarial attacks (NeurIPS 2020)☆59Updated last year
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆82Updated last year
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆26Updated 2 years ago
- Code for ICLR'2021 paper: On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections☆12Updated 3 years ago
- Official implementation of "Graph Unlearning" (ACM CCS 2022)☆40Updated last year
- Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms☆61Updated 6 months ago
- Adversarial training for Graph Neural Networks☆59Updated 3 years ago
- ☆25Updated this week
- This is the implementation of OODGAT from KDD'22: Learning on Graphs with Out-of-Distribution Nodes.☆22Updated 2 years ago
- [ICML 2023] "On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation"☆31Updated last year
- Official Code Repository for the paper - Personalized Subgraph Federated Learning (ICML 2023)☆44Updated last year
- A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).☆83Updated last month
- Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)☆19Updated last year
- ☆50Updated last week
- [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…☆16Updated last year
- Code for paper "Mixup for Node and Graph Classification", WWW 2021☆45Updated 3 years ago
- ☆52Updated 2 years ago