LFhase / GIA-HAOLinks
[ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
☆38Updated last year
Alternatives and similar repositories for GIA-HAO
Users that are interested in GIA-HAO are comparing it to the libraries listed below
Sorting:
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).☆30Updated 2 years ago
- 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] "On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation"☆33Updated last year
- G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)☆29Updated 3 years ago
- Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem☆19Updated 4 years ago
- Official implementation of GOAT model (ICML2023)☆38Updated 2 years ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆116Updated 2 years ago
- ☆26Updated 3 years ago
- source code of KDD 2022 paper "Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN".☆28Updated last year
- An official PyTorch implementation of "Certifiably Robust Graph Contrastive Learning" (NeurIPS 2023)☆11Updated last year
- Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph M…☆96Updated last year
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆92Updated 2 years ago
- [WWW2022] Geometric Graph Representation Learning via Maximizing Rate Reduction☆27Updated 3 years ago
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆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
- Code for Mind the Label Shift of Augmentation-based Graph OOD generalization (LiSA) in CVPR 2023. LiSA is a model-agnostic Graph OOD fram…☆17Updated 2 years ago
- [ICML 2023] "On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation"☆24Updated last year
- [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)☆21Updated 2 years ago
- This is the implementation of OODGAT from KDD'22: Learning on Graphs with Out-of-Distribution Nodes.☆23Updated 3 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆61Updated 2 years ago
- A curated list of resources for OOD detection with graph data.☆19Updated last year
- ☆34Updated this week
- Code for the paper "Quantifying Privacy Leakage in Graph Embedding" published in MobiQuitous 2020☆16Updated 3 years ago
- Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)☆20Updated 2 years ago
- The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"☆82Updated 2 years ago
- [ICML 2021] "A Unified Lottery Tickets Hypothesis for Graph Neural Networks", Tianlong Chen*, Yongduo Sui*, Xuxi Chen, Aston Zhang, Zhang…☆67Updated last year
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆105Updated last year
- A PyTorch implementation of "Backdoor Attacks to Graph Neural Networks" (SACMAT'21)☆40Updated 4 years ago
- [NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?☆22Updated last year
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 5 years ago