Wuyxin / ReFine
Official code of "Towards Multi-Grained Explainability for Graph Neural Networks" (NeurIPS 2021) + Pytorch Implementation of recent attribution methods for GNNs
☆69Updated last month
Alternatives and similar repositories for ReFine:
Users that are interested in ReFine are comparing it to the libraries listed below
- Source code for From Stars to Subgraphs (ICLR 2022)☆68Updated 11 months ago
- [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…☆110Updated 6 months ago
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆166Updated last year
- Ratioanle-aware Graph Contrastive Learning codebase☆40Updated last year
- Parameterized Explainer for Graph Neural Network☆130Updated last year
- Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)☆124Updated last year
- ☆55Updated 3 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆88Updated 3 years ago
- Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"☆58Updated 3 years ago
- ☆57Updated 3 years ago
- NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure☆40Updated last year
- Code for "SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism"☆55Updated 3 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆81Updated last year
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆134Updated 2 years ago
- Graph meta learning via local subgraphs (NeurIPS 2020)☆121Updated 8 months ago
- Code for ICDM2020 full paper: "Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning"☆44Updated 3 years ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Updated 2 years ago
- GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]☆192Updated last month
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆122Updated 2 years ago
- ☆63Updated 2 years ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆85Updated 2 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"☆50Updated 2 years ago
- ☆132Updated last year
- [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"☆134Updated 4 months ago
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆79Updated 2 years ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆101Updated last year
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆101Updated 2 years ago
- Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction☆66Updated 2 years ago
- Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).☆79Updated 3 years ago