wanyu-lin / ICML2021-Gem
Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."
☆65Updated 2 years ago
Alternatives and similar repositories for ICML2021-Gem:
Users that are interested in ICML2021-Gem are comparing it to the libraries listed below
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆50Updated 2 years ago
- [ICML 2022] Local Augmentation for Graph Neural Networks☆66Updated 9 months ago
- [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…☆86Updated last year
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆87Updated 3 years ago
- Generating PGM Explanation for GNN predictions☆74Updated last year
- Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)☆124Updated last year
- Reinforced Causal Explainer for Graph Neural Networks, TPAMI2022☆35Updated 2 years ago
- ☆43Updated last year
- ☆55Updated 2 years ago
- Official code of "Towards Multi-Grained Explainability for Graph Neural Networks" (NeurIPS 2021) + Pytorch Implementation of recent attri…☆68Updated 3 weeks ago
- NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure☆40Updated last year
- ☆45Updated last year
- Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR …☆34Updated this week
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆107Updated last year
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- Code for paper https://arxiv.org/abs/2102.13186☆44Updated 3 years ago
- Parameterized Explainer for Graph Neural Network☆130Updated last year
- Ratioanle-aware Graph Contrastive Learning codebase☆40Updated last year
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆163Updated last year
- ☆56Updated 3 years ago
- Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction☆66Updated 2 years ago
- Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminat…☆54Updated 2 years ago
- Graph Structured Neural Network☆39Updated 2 years ago
- A collection of papers on Graph Structural Learning (GSL)☆54Updated last year
- Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"☆78Updated 3 years ago
- GraphFramEx: a systematic evaluation framework for explainability methods on GNNs☆43Updated 11 months ago
- Official code implementation for WSDM 23 paper Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs.☆33Updated last year
- A curated list of publications and code about data augmentaion for graphs.☆64Updated 2 years ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆47Updated 2 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆81Updated last year