Awesome-Graph-OOD-Adaptation

This repository contains a list of papers on graph out-of-distribution adaptation.

Check out our survey: Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs

Contents

• Training-time Graph OOD Adaptation
  • Model-centric Approaches
  • Data-centric Approaches
• Test-time Graph OOD Adaptation
  • Model-centric Approaches
  • Data-centric Approaches
• Transferability evaluation

Training-time Graph OOD Adaptation

Model-centric Approaches

Name	Category	Paper	Code
DAGNN	Invariant Representation Learning	[ICDM 2019] Domain-Adversarial Graph Neural Networks for Text Classification	[N/A]
DANE	Invariant Representation Learning	[ICJAI 2019] DANE: Domain Adaptive Network Embedding	Unofficial
CDNE	Invariant Representation Learning	[TNNLS 2020] Network Together: Node Classification via Cross-Network Deep Network Embedding	Code
ACDNE	Invariant Representation Learning	[AAAI 2020] Adversarial Deep Network Embedding for Cross-network Node Classification	Code
UDA-GCN	Invariant Representation Learning	[TheWebConf 2020] Unsupervised Domain Adaptive Graph Convolutional Networks	Code
DGDA	Invariant Representation Learning	[TKDD 2024] Graph domain adaptation: A generative view.	Code
SR-GNN	Invariant Representation Learning	[NeurIPS 2021] Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data	Code
ASN	Invariant Representation Learning	[CIKM 2021] Adversarial separation network for cross-network node classification	Code
AdaGCN	Invariant Representation Learning	[TKDE 2022] Graph transfer learning via adversarial domain adaptation with graph convolution	Code
GraphAE	Invariant Representation Learning	[TKDE 2023] Learning adaptive node embeddings across graphs	[N/A]
GRADE	Invariant Representation Learning	[AAAI 2023] Non-iid transfer learning on graphs	Code
JHGDA	Invariant Representation Learning	[CIKM 2023] Improving graph domain adaptation with network hierarchy	Code
SGDA	Invariant Representation Learning	[ICJAI 2023] Semi-supervised Domain Adaptation in Graph Transfer Learning	Code
SRNC	Concept-shift Aware Representation Learning	[NeurIPS 2022] Shift-Robust Node Classification via Graph Clustering Co-training	[N/A]
StruRW	Concept-shift Aware Representation Learning	[ICML 2023] Structural re-weighting improves graph domain adaptation	Code
GCONDA++	Concept-shift Aware Representation Learning	[arXiv] Explaining and Adapting Graph Conditional Shift	[N/A]
KDGA	Model Regularization	[NeurIPS 2022] Knowledge distillation improves graph structure augmentation for graph neural networks	Code
SS/MFR-Reg	Model Regularization	[ICLR 2023] Graph domain adaptation via theory-grounded spectral regularization	Code
KTGNN	Model Regularization	[TheWebConf 2023] Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network	Code

Data-centric Approaches

Name	Category	Paper	Code
IW	Instance Weighting	[TheWebConf 2013] Predicting positive and negative links in signed social networks by transfer learning	[N/A]
NES-TL	Instance Weighting	[TNSE 2020] Nes-tl: Network embedding similarity-based transfer learning	[N/A]
RSS-GNN	Instance Weighting	[BIBM 2022] Reinforced Sample Selection for Graph Neural Networks Transfer Learning	[N/A]
DR-GST	Instance Weighting	[TheWebConf 2022] Confidence may cheat: Self-training on graph neural networks under distribution shift	Code
FakeEdge	Graph Transformation	[LoG 2022] Fakeedge: Alleviate dataset shift in link prediction	Code
Bridged-GNN	Graph Transformation	[CIKM 2023] Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer	Code
DC-GST	Graph Transformation	[WSDM 2024] Distribution consistency based self-training for graph neural networks with sparse labels	[N/A]

Test-time Graph OOD Adaptation

Model-centric Approaches

Name	Category	Paper	Code
GraphControl	Fine-tuning	[arXiv] GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning	[N/A]
SOGA	Fine-tuning	[WSDM 2024] Source free unsupervised graph domain adaptation	Code
GAPGC	Fine-tuning	[ICML 2022] GraphTTA: Test Time Adaptation on Graph Neural Networks	[N/A]
GT3	Parameter Sharing	[arXiv] Test-time training for graph neural networks	[N/A]
GraphGLOW	Parameter Sharing	[KDD 2023] GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks	Code

Data-centric Approaches

Name	Category	Paper	Code
FRGNN	Feature Reconstruction	[arXiv] FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction	[N/A]
GTRANS	Graph Transformation	[ICLR 2023] Empowering graph representation learning with test-time graph transformation	Code

Related: Transferability evaluation

Name	Paper	Code
EGI	[NeurIPS 2021] Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization	Code
WNN	[NeurIPS 2020] Graphon Neural Networks and the Transferability of Graph Neural Networks	[N/A]
TMD	[NeurIPS 2022] Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks	Code
W2PGNN	[KDD 2023] When to Pre-Train Graph Neural Networks? From Data Generation Perspective!	Code