ppope / explain_graphsLinks
Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR 2019)
☆34Updated 5 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:
- ☆62Updated 4 years ago
- Implementation of "Explainability Methods for Graph Convolutional Neural Networks" from HRL Laboratories☆84Updated 3 years ago
- Graph meta learning via local subgraphs (NeurIPS 2020)☆126Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆68Updated 3 years ago
- Source code for PairNorm (ICLR 2020)☆79Updated 5 years ago
- Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)☆43Updated 2 years ago
- Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "…☆124Updated 5 years ago
- Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods☆69Updated 6 months ago
- Variational Graph Convolutional Networks☆23Updated 4 years ago
- Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).☆80Updated 4 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆137Updated 2 years ago
- Official Code Repository for the paper "Accurate Learning of Graph Representations with Graph Multiset Pooling" (ICLR 2021)☆107Updated 3 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 3 years ago
- [ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆110Updated 4 years ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆116Updated last year
- The code for our ICLR paper: StructPool: Structured Graph Pooling via Conditional Random Fields☆58Updated 5 years ago
- Official implementation of our FLAG paper (CVPR2022)☆145Updated 3 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 4 years ago
- ☆28Updated 3 years ago
- Source code for From Stars to Subgraphs (ICLR 2022)☆69Updated last year
- The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)☆46Updated 4 years ago
- [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…☆113Updated 11 months ago
- Implementation of Directional Graph Networks in PyTorch and DGL☆118Updated 4 years ago
- PyTorch implementation of "Graph Convolutional Networks for Graphs Containing Missing Features"☆48Updated last year
- Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"☆97Updated 2 years ago
- ☆55Updated 3 years ago
- Pytorch implementation of differentiable group normalization (NeurIPS 2020)☆38Updated 4 years ago
- [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks☆27Updated 3 years ago
- Code of "Breaking the Limits of Message Passing Graph Neural Networks" paper published in ICML2021☆41Updated 4 years ago
- Code for "SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism"☆60Updated 4 years ago