ppope / explain_graphs
Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR 2019)
☆34Updated 2 months ago
Alternatives and similar repositories for explain_graphs
Users that are interested in explain_graphs are comparing it to the libraries listed below
Sorting:
- Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)☆42Updated 2 years ago
- Implementation of "Explainability Methods for Graph Convolutional Neural Networks" from HRL Laboratories☆83Updated 3 years ago
- ☆62Updated 4 years ago
- ☆27Updated 3 years ago
- Variational Graph Convolutional Networks☆22Updated 4 years ago
- Source code for PairNorm (ICLR 2020)☆76Updated 5 years ago
- Source code for From Stars to Subgraphs (ICLR 2022)☆70Updated last year
- Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).☆80Updated 3 years ago
- This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration".☆22Updated 3 years ago
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆51Updated 2 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- ☆60Updated 2 years ago
- ☆25Updated 5 years ago
- [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks☆27Updated 2 years ago
- Official code of "Towards Multi-Grained Explainability for Graph Neural Networks" (NeurIPS 2021) + Pytorch Implementation of recent attri…☆69Updated 2 months ago
- The code for our ICLR paper: StructPool: Structured Graph Pooling via Conditional Random Fields☆58Updated 5 years ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆111Updated last year
- Official Implementation of "GRPE: Relative Positional Encoding for Graph Transformer"☆57Updated 2 years ago
- ☆55Updated 3 years ago
- Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper☆50Updated 4 years ago
- Code of "Breaking the Limits of Message Passing Graph Neural Networks" paper published in ICML2021☆41Updated 3 years ago
- Graph meta learning via local subgraphs (NeurIPS 2020)☆122Updated 9 months ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Updated 3 years ago
- NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure☆40Updated last year
- Official Code Repository for the paper "Accurate Learning of Graph Representations with Graph Multiset Pooling" (ICLR 2021)☆106Updated 3 years ago
- Implementation of Directional Graph Networks in PyTorch and DGL☆118Updated 4 years ago
- Codes for 'From Canonical Correlation Analysis to Self-supervised Graph Neural Networks'. https://arxiv.org/abs/2106.12484☆68Updated last year
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- PyTorch implementation of "Graph Convolutional Networks for Graphs Containing Missing Features"☆47Updated last year
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 4 years ago