kaize0409 / Meta-PNLinks
PyTorch Implementation for "Meta Propagation Networks for Graph Few-shot Semi-supervised Learning" (AAAI2022)
☆29Updated 3 years ago
Alternatives and similar repositories for Meta-PN
Users that are interested in Meta-PN are comparing it to the libraries listed below
Sorting:
- The implementation of "Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization" (NeurIPS…☆21Updated 8 months ago
- Offical pytorch implementation of proposed NRGNN and Compared Methods in "NRGNN: Learning a Label Noise-Resistant Graph Neural Network on…☆42Updated 3 years ago
- Official PyTorch implementation for the following KDD2022 paper: Variational Inference for Training Graph Neural Networks in Low-Data Re…☆20Updated 2 years ago
- [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…☆92Updated last year
- Python implementation of "Unsupervised Domain Adaptive Graph Convolutional Networks", WWW-20.☆57Updated 4 years ago
- [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection☆42Updated 2 years ago
- Codes for 'From Canonical Correlation Analysis to Self-supervised Graph Neural Networks'. https://arxiv.org/abs/2106.12484☆68Updated last year
- Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning☆49Updated 2 years ago
- Pytorch Implementation of WSDM 2022 -- Graph Few-shot Class-incremental Learning☆23Updated 3 years ago
- This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Lea…☆17Updated 2 years ago
- The code of “Prototypical Graph Contrastive Learning”. [TNNLS 2022]☆24Updated 2 years ago
- [ICML 2022] "ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning"☆45Updated 3 years ago
- [IJCAI 2021] A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning".☆40Updated 2 years ago
- Implementation of CIKM2020 -- Graph Prototypical Networks for Few-shot Learning on Attributed Networks☆47Updated 4 years ago
- [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"☆80Updated 3 years ago
- Pytorch Implementation of LoG 22 [Oral] -- Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification☆17Updated 2 years ago
- Discovering Invariant Rationales for Graph Neural Networks (ICLR 2022)☆128Updated 2 years ago
- AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators (AAAI 2022)☆47Updated 3 years ago
- Code for "Graph Structure Learning with Variational Information Bottleneck" published in AAAI 2022☆37Updated 3 years ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆115Updated last year
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆88Updated 3 years ago
- Official repository for ICLR'23 paper: Multi-task Self-supervised Graph Neural Network Enable Stronger Task Generalization☆39Updated 2 years ago
- Code for ECML-PKDD 2022 paper "GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Cont…☆23Updated 2 years ago
- [NeurIPS 2022] "Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative" by Tianxin Wei, Yuning You, Tianlong Chen, Y…☆60Updated 2 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆59Updated 2 years ago
- StableGNN-Generalizing Graph Neural Networks on Out-Of-Distribution Graphs☆23Updated last year
- This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It…☆25Updated 3 years ago
- [ICML 2022] Local Augmentation for Graph Neural Networks☆66Updated last year
- Ratioanle-aware Graph Contrastive Learning codebase☆43Updated 2 years ago
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆45Updated last year