lukasjf / contrastive-gnn-explanationLinks
☆19Updated 2 years ago
Alternatives and similar repositories for contrastive-gnn-explanation
Users that are interested in contrastive-gnn-explanation are comparing it to the libraries listed below
Sorting:
- Parameterized Explainer for Graph Neural Network☆136Updated last year
- This is a Pytorch implementation of GraphLIME☆92Updated 3 years ago
- Generating PGM Explanation for GNN predictions☆75Updated 2 years ago
- Official implementation of our FLAG paper (CVPR2022)☆145Updated 3 years ago
- PyTorch implementation of "Graph Convolutional Networks for Graphs Containing Missing Features"☆48Updated last year
- ☆32Updated 2 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆136Updated 2 years ago
- ☆56Updated 3 years ago
- Code for paper https://arxiv.org/abs/2102.13186☆43Updated 4 years ago
- Graph Representation Learning via Graphical Mutual Information Maximization☆116Updated 5 years ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆67Updated 3 years ago
- Representation Learning on Graphs with Jumping Knowledge Networks☆39Updated 6 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆114Updated 3 years ago
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 4 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆88Updated 3 years ago
- Code for reproducing results in GraphMix paper☆72Updated 2 years ago
- A collection of graph classification methods☆42Updated 4 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆89Updated 3 years ago
- ☆60Updated 3 years ago
- Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods☆69Updated 5 months ago
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆169Updated last year
- Graph meta learning via local subgraphs (NeurIPS 2020)☆125Updated 11 months ago
- Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".☆43Updated 4 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆103Updated 3 weeks ago
- Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddin…☆230Updated last year
- Defending graph neural networks against adversarial attacks (NeurIPS 2020)☆70Updated 2 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆83Updated 10 months ago
- Hypergraph representation learning: Hypergraph Networks with Hyperedge Neurons.☆41Updated 4 years ago
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆53Updated 2 years ago
- ☆138Updated 4 years ago