Alternatives and similar repositories for wsdm_GDN

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

• yixinliu233 / GREET
  [AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
  ☆52Updated 2 years ago
• RecklessRonan / GloGNN
  ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
  ☆43Updated 3 years ago
• jhkim611 / CLAD
  The official source code for "Class Label-aware Graph Anomaly Detection", accepted at CIKM 2023.
  ☆16Updated last year
• likuanppd / STABLE
  source code of KDD 2022 paper "Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN".
  ☆28Updated last year
• yongduosui / AIA
  [NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Q…
  ☆17Updated last year
• Xzh0u / GBK-GNN
  Code for GBK-GNN (paper accepted by WWW2022)
  ☆16Updated 3 years ago
• yuwvandy / FairVGNN
  ☆26Updated 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…
  ☆55Updated 2 years ago
• blacksingular / GHRN
  [WWW 2023] "Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum" by Yuan Gao, Xiang Wang, Xiangnan He, Zhe…
  ☆39Updated last year
• yixinliu233 / G-OOD-D
  [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
  ☆42Updated 2 years ago
• KellyGong / SparseGAD
  ☆18Updated 2 years ago
• zjunet / AMNet
  ☆38Updated 3 years ago
• yushundong / PyGDebias
  Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms
  ☆63Updated last year
• Graph-COM / GAD-NR
  [WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction
  ☆48Updated 8 months ago
• SitaoLuan / ACM-GNN
  NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…
  ☆84Updated 8 months ago
• TrustAGI-Lab / ANEMONE
  [CIKM 2021] A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning".
  ☆49Updated 3 years ago
• Zjer12 / Sub
  ☆14Updated 2 years ago
• GemsLab / HeteRobust
  How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications (KDD'22)
  ☆13Updated 3 years ago
• yixinliu233 / D2PT
  [KDD'23] Learning Strong Graph Neural Networks with Weak Information
  ☆42Updated 2 years ago
• BUPT-GAMMA / Graph-Structure-Estimation-Neural-Networks
  Source code for WWW 2021 paper "Graph Structure Estimation Neural Networks"
  ☆58Updated 4 years ago
• EnyanDai / NRGNN
  Offical pytorch implementation of proposed NRGNN and Compared Methods in "NRGNN: Learning a Label Noise-Resistant Graph Neural Network on…
  ☆42Updated 3 years ago
• hedongxiao-tju / HOG-GCN
  ☆38Updated 3 years ago
• Yujun-Yan / Heterophily_and_oversmoothing
  Codes for "Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks"
  ☆38Updated 2 years ago
• jindi-tju / U-GCN
  Source code of "NeurIPS21 - Universal Graph Convolutional Networks"
  ☆20Updated 3 years ago
• zhoushengisnoob / CKD
  Code for The Web Conference 2022 Paper "Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding"
  ☆17Updated 3 years ago
• zjlxgxz / DynAnom
  Codebase for KDD22 paper "Subset Node Anomaly Tracking over Large Dynamic Graphs"
  ☆15Updated 2 years ago
• shifengzhao / H2-FDetector
  ☆31Updated 3 years ago
• yifeiacc / COSTA
  Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
  ☆49Updated 2 years ago
• EnyanDai / RSGNN
  An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))
  ☆33Updated 3 years ago
• zjunet / GPF
  The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.
  ☆33Updated last year