Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods
☆69Feb 16, 2025Updated last year
Alternatives and similar repositories for ReFine
Users that are interested in ReFine are comparing it to the libraries listed below
Sorting:
- Parameterized Explainer for Graph Neural Network☆142Feb 23, 2024Updated 2 years ago
- ☆10Jun 14, 2025Updated 8 months ago
- a robust metric (robust fidelity) for XGNN (ICLR24)☆12Jun 3, 2025Updated 9 months ago
- Official Implementation of "D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion"☆24Oct 29, 2023Updated 2 years ago
- Papers about explainability of GNNs☆789Jan 1, 2026Updated 2 months ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆49Jun 3, 2022Updated 3 years ago
- GraphFramEx: a systematic evaluation framework for explainability methods on GNNs☆50Apr 8, 2024Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Apr 7, 2022Updated 3 years ago
- Generating PGM Explanation for GNN predictions☆75Jul 6, 2023Updated 2 years ago
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆174Feb 19, 2024Updated 2 years ago
- Reinforced Causal Explainer for Graph Neural Networks, TPAMI2022☆41Jun 13, 2022Updated 3 years ago
- (ICLR 2022) Discovering Invariant Rationales for Graph Neural Networks☆131Jul 15, 2023Updated 2 years ago
- GraphXAI: Resource to support the development and evaluation of GNN explainers☆205May 22, 2024Updated last year
- Size-Invariant Graph Representations for Graph Classification Extrapolations (ICML 2021 Long Talk)☆23Jan 26, 2023Updated 3 years ago
- ☆11Nov 8, 2023Updated 2 years ago
- (ICML 2023) Discover and Cure: Concept-aware Mitigation of Spurious Correlation☆44Nov 17, 2025Updated 3 months ago
- ☆10Jun 11, 2023Updated 2 years ago
- The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021☆36Nov 29, 2021Updated 4 years ago
- GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts (NeurIPS 2024)☆30Mar 1, 2025Updated last year
- Ratioanle-aware Graph Contrastive Learning codebase☆44Jun 15, 2023Updated 2 years ago
- ☆11Jul 12, 2022Updated 3 years ago
- Graph Homomorphism Convolution (ICML'20)☆12Jul 6, 2023Updated 2 years ago
- Implementation of "Explainability Methods for Graph Convolutional Neural Networks" from HRL Laboratories☆84Oct 19, 2021Updated 4 years ago
- Implementation fo Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks☆16Aug 31, 2022Updated 3 years ago
- personalized recommendation☆12Mar 26, 2020Updated 5 years ago
- gnn explainer☆1,031Aug 30, 2024Updated last year
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆98Apr 25, 2022Updated 3 years ago
- Source Code & Datasets for "FBL: Feature-Balanced Loss for Long-Tailed Visual Recognition"☆13Sep 3, 2022Updated 3 years ago
- Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks.…☆35Apr 2, 2022Updated 3 years ago
- Official Code of Decoupled Graph Convolution (DGC)☆16Jan 31, 2026Updated last month
- A library for graph deep learning research☆2,001Jul 15, 2024Updated last year
- ☆38Sep 23, 2021Updated 4 years ago
- A curated list of resources for OOD detection with graph data.☆19Dec 30, 2023Updated 2 years ago
- MetA-Train to Explain☆18Feb 15, 2022Updated 4 years ago
- [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…☆17Nov 6, 2023Updated 2 years ago
- [ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.☆24Jul 18, 2023Updated 2 years ago
- ☆18Mar 1, 2022Updated 4 years ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆121Aug 28, 2023Updated 2 years ago
- [WSDM 2023] "Alleviating Structrual Distribution Shift in Graph Anomaly Detection" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, H…☆25Jun 2, 2023Updated 2 years ago