danielzuegner / robust-gcn-structureLinks
☆12Updated 4 years ago
Alternatives and similar repositories for robust-gcn-structure
Users that are interested in robust-gcn-structure are comparing it to the libraries listed below
Sorting:
- Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".☆43Updated 4 years ago
- Adversarial Attacks on Node Embeddings via Graph Poisoning☆59Updated 5 years ago
- Implementation of the certificates proposed in the paper "Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized …☆35Updated last year
- Adversarial training for Graph Neural Networks☆60Updated 4 years ago
- Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.☆54Updated 5 years ago
- ☆18Updated 3 years ago
- Adversarial Attack on Graph Structured Data (https://arxiv.org/abs/1806.02371)☆128Updated 2 years ago
- Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".☆149Updated 3 years ago
- Implementation of paper "Transferring Robustness for Graph Neural Network Against Poisoning Attacks".☆20Updated 5 years ago
- Certifiable Robustness to Graph Perturbations☆13Updated 5 years ago
- Implementation of SBM-meet-GNN☆23Updated 6 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆37Updated 4 years ago
- ☆19Updated 2 years ago
- Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs☆34Updated 5 years ago
- A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning☆12Updated 5 years ago
- G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)☆30Updated 3 years ago
- A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models☆35Updated 4 years ago
- Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure☆20Updated 5 years ago
- ☆44Updated 4 years ago
- Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.☆30Updated 5 years ago
- Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"☆52Updated 5 years ago
- Code for ICML2019 Paper "Compositional Invariance Constraints for Graph Embeddings"☆50Updated 5 years ago
- Implementation of Adversarial Privacy Graph Embedding in TensorFlow☆20Updated 5 years ago
- ☆13Updated 4 years ago
- Pytorch implementation of gnn meta attack (mettack). Paper title: Adversarial Attacks on Graph Neural Networks via Meta Learning.☆21Updated 4 years ago
- Implement of DiGCN, NeurIPS-2020☆47Updated 4 years ago
- ☆35Updated 6 years ago
- [ICML 2021] Information Obfuscation of Graph Neural Networks☆36Updated 3 years ago
- This is a sample implementation of "TIMERS: Error-Bounded SVD Restart on Dynamic Networks"(AAAI 2018).☆12Updated 6 years ago
- Code for the paper "Unsupervised Community Detection with Modularity-based Attention Model"☆36Updated 5 years ago