Zhen-Tan-dmml / TLP-FSNC
Pytorch Implementation of LoG 22 [Oral] -- Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
☆14Updated last year
Related projects ⓘ
Alternatives and complementary repositories for TLP-FSNC
- [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection☆35Updated last year
- PyTorch Implementation for "Meta Propagation Networks for Graph Few-shot Semi-supervised Learning" (AAAI2022)☆29Updated 2 years ago
- Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning☆46Updated last year
- [AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating☆49Updated last year
- The official source code for Task-Equivariant Graph Few-shot Learning (TEG) at KDD 2023.☆23Updated 11 months ago
- ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily☆42Updated 2 years ago
- GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)☆24Updated 4 months ago
- This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Lea…☆17Updated last year
- Pytorch implementation of NeurIPS-23:"Structure-free Graph Condensation (SFGC): From Large-scale Graphs to Condensed Graph-free Data"☆24Updated last year
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆53Updated last year
- [ICML 2022] "ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning"☆41Updated 2 years ago
- Code for ECML-PKDD 2022 paper "GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Cont…☆22Updated last year
- [ICLR 2023] "Graph Domain Adaptation via Theory-Grounded Spectral Regularization" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆21Updated last year
- Official Code: TheWebConf 2022 Compact Graph Structure Learning via Mutual Information Compression☆24Updated 7 months ago
- Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".☆25Updated 2 years ago
- Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"☆58Updated 3 years ago
- Offical pytorch implementation of proposed NRGNN and Compared Methods in "NRGNN: Learning a Label Noise-Resistant Graph Neural Network on…☆40Updated 2 years ago
- The official source code for Similarity Preserving Adversarial Graph Contrastive Learning (SP-AGCL) at KDD 2023.☆23Updated 9 months ago
- "GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification" in KDD'23☆27Updated 7 months ago
- The official source code for "LTE4G: Long-Tail Experts for Graph Neural Networks" paper, accepted at CIKM 2022.☆38Updated 2 years ago
- Code for "Graph Structure Learning with Variational Information Bottleneck" published in AAAI 2022☆32Updated 2 years ago
- [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"☆43Updated 10 months ago
- The official source code for "GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignm…☆24Updated 2 years ago
- [ICML 2023] Linkless Link Prediction via Relational Distillation☆19Updated last year
- [WSDM 2023] "Alleviating Structrual Distribution Shift in Graph Anomaly Detection" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, H…☆20Updated last year
- A curated list of papers and code related to class-imbalanced learning on graphs (CILG).☆32Updated 9 months ago
- The official source code for "S-Mixup: Structural Mixup for Graph Neural Networks", accepted at CIKM 2023 (Short Paper).☆18Updated last year
- [WWW 2023] "Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum" by Yuan Gao, Xiang Wang, Xiangnan He, Zhe…☆34Updated last year
- Code for "Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing"☆15Updated last year
- Code & data for ICLR'23 Spotlight paper "Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency".☆29Updated last year