Alternatives and similar repositories for Improving-Subgraph-Recognition-with-Variation-Graph-Information-Bottleneck-VGIB-

Users that are interested in Improving-Subgraph-Recognition-with-Variation-Graph-Information-Bottleneck-VGIB- are comparing it to the libraries listed below

• lsh0520 / RGCL
  Ratioanle-aware Graph Contrastive Learning codebase
  ☆44Updated 2 years ago
• LFhase / CIGA
  [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
  ☆116Updated 2 years ago
• wondergo2017 / DIDA
  Implementation Codes for NeurIPS22 paper "Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift"
  ☆22Updated 2 years ago
• ChandlerBang / DeCorr
  [KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"
  ☆45Updated last year
• ChandlerBang / GTrans
  [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
  ☆61Updated 2 years ago
• RecklessRonan / GloGNN
  ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
  ☆44Updated 3 years ago
• GentleZhu / Shift-Robust-GNNs
  "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')
  ☆48Updated 3 years ago
• ahxt / g-mixup
  [ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification
  ☆106Updated last year
• junxia97 / SimGRACE
  [WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
  ☆81Updated 3 years ago
• Graph-COM / StruRW
  [ICML 2023] Structural Re-weighting Improves Graph Domain Adaptation (StruRW)
  ☆21Updated 2 years ago
• divelab / GOOD
  GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
  ☆202Updated 6 months ago
• susheels / adgcl
  Adversarial Graph Augmentation to Improve Graph Contrastive Learning
  ☆89Updated 3 years ago
• Amanda-Zheng / Auto-HeG
  Pytorch implementation of WWW'23:"Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs"
  ☆15Updated 2 years ago
• LFhase / GALA
  [NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
  ☆22Updated last year
• Samyu0304 / graph-information-bottleneck-for-Subgraph-Recognition
  ☆56Updated 2 years ago
• qitianwu / GraphOOD-EERM
  The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
  ☆92Updated 2 years ago
• zjunet / GPF
  The code Implementation of the paper “Universal Prompt Tuning for Graph Neural Networks”.
  ☆34Updated last year
• Amanda-Zheng / LEBED
  Pytorch implementation for ICLR24:"Online GNN Evaluation Under Test-Time Graph Distribution Shifts"
  ☆16Updated last year
• AndyJZhao / GLEM
  ☆129Updated 5 months ago
• tengxiao1 / GraphACL
  GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)
  ☆31Updated last year
• Namkyeong / BGRL_Pytorch
  Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"
  ☆81Updated 3 years ago
• liun-online / CoGSL
  Official Code: TheWebConf 2022 Compact Graph Structure Learning via Mutual Information Compression
  ☆24Updated last year
• LechengKong / gdgnn
  ☆19Updated 2 years ago
• yongduosui / CAL
  [KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, X…
  ☆93Updated last year
• yixinliu233 / G-OOD-D
  [WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
  ☆43Updated 2 years ago
• Graph-COM / GSAT
  [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
  ☆168Updated last year
• zaixizhang / ProtGNN
  Official implementation of AAAI'22 paper "ProtGNN: Towards Self-Explaining Graph Neural Networks"
  ☆53Updated 2 years ago
• Graph-COM / Neural_Higher-order_Pattern_Prediction
  ☆14Updated 3 years ago
• BorealisAI / SLAPS-GNN
  PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"
  ☆89Updated 3 years ago
• Zhen-Tan-dmml / TLP-FSNC
  Pytorch Implementation of LoG 22 [Oral] -- Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
  ☆17Updated 2 years ago