Graph Domain Adaptation

Latest papers

9 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Graph Domain Adaptation Does Not End with Representation Learning
Sep 22, 2026Ziqian Liu, Yongxue Xu, Enze Zhang +3Unsupervised Domain Adaptation Graph Representation Learning
Sun Yat-sen University, China · University of Wisconsin–Madison, USA · Jiangsu University, China +2
Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning
Sep 12, 2026Yingxu Wang, Kunyu Zhang, Siyang GaoGraph Domain Adaptation Domain Adaptation
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Zhengzhou University, Zhengzhou, China · City University of Hong Kong, Hong Kong SAR, China
Multi-Source Multi-View Graph Domain Adaptation with Hyperbolic Residual Encoding for Cross-Site MDD Identification from rs-fMRI
Jul 31, 2026Zhanpeng Zheng, Xiran Chen, Haiteng Jiang +6Multi-Source Domain Adaptation Domain Adaptation
School of Computer and Artificial Intelligence, Shandong Jianzhu University, Shandong, China. · School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China. · Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. +4
Cross-Resolution Semantic Learning for Graph Domain Adaptation
Jul 31, 2026Yingxu Wang, Haoze Huang, Zhongkai Zheng +1Unsupervised Domain Adaptation Graph Representation Learning
Mohamed bin Zayed University of Artificial Intelligence · Fuzhou University · Sun Yat-sen University +1
Selectivity Matters: Source Node Influence Pruning for Unsupervised Graph Domain Adaptation
Jul 20, 2026Ridong Han, Yawen Shen, Zhongnian Li +3Unsupervised Domain Adaptation Domain Adaptation
School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221116, China · Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221116, China · Jiangsu Provincial Industrial Technology Engineering Center for Intelligent Sensing and Emergency IoT in Underground Space, Xuzhou 221116, China +1
GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning
May 14, 2026Yan Jiang, Ruihong Qiu, Zi HuangDomain Adaptation Graph Domain Adaptation
School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Queensland, Australia
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
Apr 3, 2026Yingxu Wang, Kunyu Zhang, Jiaxin Huang +4Graph Domain Adaptation Domain Adaptation
Zhengzhou University · The Hebei University of Technology · California State Polytechnic University +1
Dual-branch Graph Domain Adaptation for Cross-scenario Multi-modal Emotion Recognition
Mar 27, 2026Yuntao Shou, Jun Zhou, Tao Meng +2Graph Domain Adaptation Emotion Recognition in Conversations
College of Computer and Mathematics, Central South University of Forestry and Technology · Department of Computer Science, State University of New York
A Theory of Random Graph Shift in Truncated-Spectrum vRKHS
Feb 27, 2026Zhang Wan, Tingting Mu, Samuel KaskiGraph Domain Adaptation Distribution Shift Robustness
Department of Computer Science, University of Manchester, UK · Department of Computer Science, Aalto University, Finland · ELLIS Institute Finland

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Graph Domain Adaptation Does Not End with Representation Learning

Ziqian Liu, Yongxue Xu, Enze Zhang, Jiaqi Zhang+2

Sun Yat-sen University, China · University of Wisconsin–Madison, USA · Jiangsu University, China+2

Unsupervised Domain Adaptation Graph Representation Learning Domain Adaptation Graph Domain Adaptation Node Classification

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Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph under shifts in both node attributes and graph structure. Existing methods primarily adapt graph representations through propagation redesign, distribution alignment, or source-to-target transition modeling, but still rely on a single graph-propagating path for target prediction. This leaves open whether an adapted graph representation exhausts the predictive evidence available in the target domain, since the graph-aware expert and graph-free local expert may exhibit different failure modes under topological shifts. To address this limitation, we propose EviGDA, an Evidence-Augmented Graph Domain Adaptation framework that complements graph representation adaptation with a graph-free local expert. The graph-aware expert performs message passing and entropy-aware marginal alignment, while the graph-free local expert learns solely from source node features and labels without graph propagation or target alignment. The two experts are optimized independently and combined only at inference through a task-level constant probability mixture, preserving complementary evidence without joint training, learned routing, or target pseudo-labels. Extensive experiments on ten datasets and 16 transfer tasks show that EviGDA outperforms state-of-the-art baselines. Complementary prediction paths add value beyond graph representation alignment.