abojchevski / sparse_smoothingLinks
Implementation of the certificates proposed in the paper "Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More"
☆36Updated 2 years ago
Alternatives and similar repositories for sparse_smoothing
Users that are interested in sparse_smoothing are comparing it to the libraries listed below
Sorting:
- Adversarial Attacks on Node Embeddings via Graph Poisoning☆60Updated 6 years ago
- Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".☆43Updated 5 years ago
- Official implementation of our FLAG paper (CVPR2022)☆145Updated 3 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆105Updated 5 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆98Updated 3 years ago
- Implicit Graph Neural Networks☆64Updated 4 years ago
- Code for reproducing results in GraphMix paper☆72Updated 3 years ago
- Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"☆53Updated 5 years ago
- ☆12Updated 5 years ago
- Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".☆154Updated 4 years ago
- ☆19Updated 3 years ago
- Adversarial Attack on Graph Structured Data (https://arxiv.org/abs/1806.02371)☆130Updated 3 years ago
- Adversarial training for Graph Neural Networks☆61Updated 4 years ago
- Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"☆42Updated 4 years ago
- Equivalence Between Structural Representations and Positional Node Embeddings☆22Updated 5 years ago
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).☆31Updated 2 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆86Updated last year
- Source code for PairNorm (ICLR 2020)☆79Updated 5 years ago
- A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).☆90Updated last year
- ☆62Updated 5 years ago
- ☆30Updated 4 years ago
- ☆34Updated 5 years ago
- Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs☆34Updated 5 years ago
- PyTorch Implementation of GraphTSNE, ICLR’19☆137Updated 6 years ago
- Code for Graphite iterative graph generation☆59Updated 6 years ago
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆73Updated 6 years ago
- Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph M…☆98Updated 2 years ago
- G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)☆29Updated 3 years ago
- Pytorch Implementation of Graph Convolutional Kernel Networks☆54Updated 3 years ago
- [ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆112Updated 4 years ago