# On the Benefits of Attribute-Driven Graph Domain Adaptation

arXiv: https://arxiv.org/abs/2502.06808 · HTML: https://mlsanity.com/paper/2502.06808/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 49 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 30 of 32)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Continual Test-Time Domain Adaptation](https://mlsanity.com/paper/2203.13591/) (2022)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [Generalized Source-free Domain Adaptation](https://mlsanity.com/paper/2108.01614/) (2021)
- [AM-GCN: Adaptive Multi-channel Graph Convolutional Networks](https://mlsanity.com/paper/2007.02265/) (2020)
- [Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization](https://mlsanity.com/paper/2009.05204/) (2020)
- [Subgroup Generalization and Fairness of Graph Neural Networks](https://mlsanity.com/paper/2106.15535/) (2021)
- [Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution](https://mlsanity.com/paper/1909.01541/) (2019)
- [Adversarial Deep Network Embedding for Cross-network Node Classification](https://mlsanity.com/paper/2002.07366/) (2020)
- [DANE: Domain Adaptive Network Embedding](https://mlsanity.com/paper/1906.00684/) (2019)
- [Non-IID Transfer Learning on Graphs](https://mlsanity.com/paper/2212.08174/) (2022)
- [Rethinking Propagation for Unsupervised Graph Domain Adaptation](https://mlsanity.com/paper/2402.05660/) (2024)
- [Revisiting Link Prediction: A Data Perspective](https://mlsanity.com/paper/2310.00793/) (2023)
- [PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering](https://mlsanity.com/paper/2312.14438/) (2023)
- [Semi-supervised Domain Adaptation in Graph Transfer Learning](https://mlsanity.com/paper/2309.10773/) (2023)
- [Pairwise Alignment Improves Graph Domain Adaptation](https://mlsanity.com/paper/2403.01092/) (2024)
- [Graph-Relational Domain Adaptation](https://mlsanity.com/paper/2202.03628/) (2022)
- [Structure-Preserving Graph Representation Learning](https://mlsanity.com/paper/2209.00793/) (2022)
- [Weakly Supervised Object Localization as Domain Adaption](https://mlsanity.com/paper/2203.01714/) (2022)
- [SA-GDA: Spectral Augmentation for Graph Domain Adaptation](https://mlsanity.com/paper/2408.09189/) (2024)
- [CDC: A Simple Framework for Complex Data Clustering](https://mlsanity.com/paper/2403.03670/) (2024)
- [Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering](https://mlsanity.com/paper/2312.14066/) (2023)
- [ALEX: Towards Effective Graph Transfer Learning with Noisy Labels](https://mlsanity.com/paper/2309.14673/) (2023)
- [Graph Domain Adaptation: Challenges, Progress and Prospects](https://mlsanity.com/paper/2402.00904/) (2024)
- [Domain-adaptive Message Passing Graph Neural Network](https://mlsanity.com/paper/2308.16470/) (2023)
- [Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation](https://mlsanity.com/paper/2010.12184/) (2020)
- [One Node One Model: Featuring the Missing-Half for Graph Clustering](https://mlsanity.com/paper/2412.09902/) (2024)
- [Provable Filter for Real-world Graph Clustering](https://mlsanity.com/paper/2403.03666/) (2024)
- [Network Together: Node Classification via Cross network Deep Network Embedding](https://mlsanity.com/paper/1901.07264/) (2019)

## Built on by (most cited first, 19 of 19)

- [Homophily Enhanced Graph Domain Adaptation](https://mlsanity.com/paper/2505.20089/) (2025)
- [Understanding and Mitigating Under-Confidence in GNNs from the Final Layer](https://mlsanity.com/paper/2505.11335/) (2025)
- [When Priors Backfire: On the Vulnerability of Unlearnable Examples to Pretraining](https://mlsanity.com/paper/2603.04731/) (2026)
- [Structure-Centric Graph Foundation Model via Geometric Bases](https://mlsanity.com/paper/2605.08689/) (2026)
- [DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation](https://mlsanity.com/paper/2602.00656/) (2026)
- [Learning Structure-Semantic Evolution Trajectories for Graph Domain Adaptation](https://mlsanity.com/paper/2602.10506/) (2026)
- [Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure](https://mlsanity.com/paper/2602.07573/) (2026)
- [Full spectrum Unlearnable Examples via Spectral Equalization](https://mlsanity.com/paper/2606.26719/) (2026)
- [DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation](https://mlsanity.com/paper/2604.03154/) (2026)
- [Entropy-Guided Dynamic Tokens for Graph-LLM Alignment in Molecular Understanding](https://mlsanity.com/paper/2602.02742/) (2026)
- [Learning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptation](https://mlsanity.com/paper/2602.10489/) (2026)
- [DIVER: Diving Deeper into Distilled Data via Expressive Semantic Recovery](https://mlsanity.com/paper/2605.12649/) (2026)
- [Cross-Resolution Semantic Learning for Graph Domain Adaptation](https://mlsanity.com/paper/2607.29365/) (2026)
- [Empowering GNNs for Domain Adaptation via Denoising Target Graph](https://mlsanity.com/paper/2512.06236/) (2025)
- [Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning](https://mlsanity.com/paper/2609.14045/) (2026)
- [Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport](https://mlsanity.com/paper/2609.23547/) (2026)
- [ARM: Attention with Routed-Memory for Learnable Sparse Control](https://mlsanity.com/paper/2609.24417/) (2026)
- [Graph Domain Adaptation Does Not End with Representation Learning](https://mlsanity.com/paper/2609.25692/) (2026)
- [Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting](https://mlsanity.com/paper/2609.32143/) (2026)

## Abstract (arXiv)

Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain alignment. Specifically, we first reveal the impact of node attributes for GDA by theoretically proving that in addition to the graph structural divergence between the domains, the node attribute discrepancy also plays a critical role in GDA. Moreover, we also empirically show that the attribute shift is more substantial than the topology shift, which further underscores the importance of node attribute alignment in GDA. Inspired by this finding, a novel cross-channel module is developed to fuse and align both views between the source and target graphs for GDA. Experimental results on a variety of benchmarks verify the effectiveness of our method.