divelab / GOODLinks
GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
☆204Updated 10 months ago
Alternatives and similar repositories for GOOD
Users that are interested in GOOD are comparing it to the libraries listed below
Sorting:
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆173Updated last year
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆120Updated 2 years ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆92Updated 3 years ago
- Papers about out-of-distribution generalization on graphs.☆168Updated 2 years ago
- [WWW 2021 GLB] New Benchmarks for Learning on Non-Homophilous Graphs☆116Updated 4 years ago
- [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)☆21Updated 2 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 3 years ago
- [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification☆105Updated last year
- [ICLR'22][KDD'22][IJCAI'24][NeurIPS'25] Implementation of "Graph Condensation for Graph Neural Networks"☆141Updated 2 months ago
- Ratioanle-aware Graph Contrastive Learning codebase☆44Updated 2 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆84Updated 2 years ago
- Rex Ying's Ph.D. Thesis, Stanford University☆41Updated 3 years ago
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs".☆91Updated last year
- Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods☆70Updated 10 months ago
- NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure☆44Updated 2 years ago
- ☆55Updated 3 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆138Updated 3 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆107Updated 6 months ago
- Long Range Graph Benchmark, NeurIPS 2022 Track on D&B☆161Updated 2 years ago
- [ICML 2022] Local Augmentation for Graph Neural Networks☆66Updated last year
- [ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Yo…☆116Updated last year
- A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?☆122Updated 2 years ago
- ☆58Updated 3 years ago
- ☆91Updated 2 years ago
- Adversarial Graph Augmentation to Improve Graph Contrastive Learning☆92Updated 4 years ago
- Source code for From Stars to Subgraphs (ICLR 2022)☆71Updated last year
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆81Updated 3 years ago
- This is an official implementation for "GRIT: Graph Inductive Biases in Transformers without Message Passing".☆129Updated 4 months ago
- ☆139Updated 2 years ago
- Official implementation of our FLAG paper (CVPR2022)☆145Updated 3 years ago