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 5 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆105Updated 5 years ago
- Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".☆43Updated 4 years ago
- Official implementation of our FLAG paper (CVPR2022)☆145Updated 3 years ago
- Equivalence Between Structural Representations and Positional Node Embeddings☆22Updated 5 years ago
- Code for reproducing results in GraphMix paper☆72Updated 2 years ago
- Implicit Graph Neural Networks☆64Updated 3 years ago
- Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"☆53Updated 5 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆96Updated 3 years ago
- Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs☆34Updated 5 years ago
- Pytorch Implementation of Graph Convolutional Kernel Networks☆54Updated 2 years ago
- Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".☆152Updated 3 years ago
- ☆62Updated 4 years ago
- This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).☆30Updated 2 years ago
- ☆12Updated 4 years ago
- Code for "Weisfeiler and Leman go sparse: Towards higher-order graph embeddings"☆22Updated 3 years ago
- Source code for PairNorm (ICLR 2020)☆79Updated 5 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆84Updated last year
- ☆28Updated 4 years ago
- Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"☆42Updated 4 years ago
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 4 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Code for Optimal Transport for structured data with application on graphs☆102Updated 2 years ago
- informal exposition of Weisfeiler-Leman similarity☆28Updated 4 years ago
- The official implementation of ''Can Graph Neural Networks Count Substructures?'' NeurIPS 2020☆35Updated 4 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 2 years ago
- [ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆111Updated 4 years ago
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆72Updated 6 years ago
- ☆25Updated 4 years ago
- [TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*,…☆125Updated 3 years ago