Alternatives and similar repositories for CTAug

Users that are interested in CTAug are comparing it to the libraries listed below

• Amanda-Zheng / SFGC
  Pytorch implementation of NeurIPS-23:"Structure-free Graph Condensation (SFGC): From Large-scale Graphs to Condensed Graph-free Data"
  ☆37Updated 2 years ago
• yixinliu233 / GREET
  [AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
  ☆53Updated 3 years ago
• blacksingular / wsdm_GDN
  [WSDM 2023] "Alleviating Structrual Distribution Shift in Graph Anomaly Detection" by Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, H…
  ☆25Updated 2 years ago
• BUPT-GAMMA / Uncovering-the-Structural-Fairness-in-Graph-Contrastive-Learning
  Source code for NeurIPS 2022 paper "Uncovering the Structural Fairness in Graph Contrastive Learning"
  ☆29Updated 3 years ago
• KellyGong / SparseGAD
  ☆18Updated 2 years ago
• wenzhilics / GraphSHA
  "GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification" in KDD'23
  ☆31Updated last year
• yifeiacc / COSTA
  Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
  ☆49Updated 2 years ago
• yixinliu233 / D2PT
  [KDD'23] Learning Strong Graph Neural Networks with Weak Information
  ☆43Updated 2 years ago
• rollingstonezz / Curriculum_learning_for_GNNs
  This is the official repository for NeurIPS 2023 paper "Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First"
  ☆16Updated 2 years ago
• CurvCluster / Congregate
  ☆11Updated 2 years ago
• zepengzhang / awesome-graph-structure-learning
  A collection of papers on Graph Structural Learning (GSL)
  ☆56Updated last year
• EnyanDai / RSGNN
  An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))
  ☆34Updated 3 years ago
• zyzisastudyreallyhardguy / Graph-Group-Discrimination
  Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminat…
  ☆56Updated 2 years ago
• zhoushengisnoob / CKD
  Code for The Web Conference 2022 Paper "Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding"
  ☆17Updated 3 years ago
• qiaoyu-tan / S2GAE
  ☆50Updated 2 years ago
• hedongxiao-tju / HOG-GCN
  ☆38Updated 3 years ago
• XweiQ / Benchmark-GraphFairness
  The repository of "Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark" (KDD'24)
  ☆12Updated last year
• RingBDStack / SGDD
  Code for SGDD
  ☆26Updated 2 years ago
• likuanppd / STABLE
  source code of KDD 2022 paper "Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN".
  ☆28Updated last year
• galogm / HoLe
  CIKM 23 Oral - HoLe: Homophily-enhanced Structure Learning for Graph Clustering
  ☆10Updated last year
• EnyanDai / NRGNN
  Offical pytorch implementation of proposed NRGNN and Compared Methods in "NRGNN: Learning a Label Noise-Resistant Graph Neural Network on…
  ☆45Updated 3 years ago
• chirag-agarwall / nifty
  Code for paper https://arxiv.org/abs/2102.13186
  ☆43Updated 4 years ago
• zhiming-xu / conad
  Contrastive Attributed Network Anomaly Detection with Data Augmentation (PAKDD'22)
  ☆28Updated 3 years ago
• FraudDetection / FRAUDRE
  ☆37Updated 3 years ago
• yushundong / PyGDebias
  Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms
  ☆65Updated last year
• RecklessRonan / GloGNN
  ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
  ☆45Updated 3 years ago
• junxia97 / ProGCL
  [ICML 2022] "ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning"
  ☆46Updated 3 years ago
• yixinliu233 / G-OOD-D
  [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
  ☆45Updated 2 years ago
• zjunet / GPF
  The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.
  ☆35Updated 2 years ago
• yuwvandy / FairVGNN
  ☆27Updated 3 years ago