Gradual Domain Adaptation for Graph Learning
Year: 2026
Type: article
Abstract: Existing machine learning literature lacks graph-based domain adaptation techniques capable of handling large distribution shifts, primarily due to the difficulty in simulating a coherent evolutionary path from source to target graph. To meet this challenge, we present a graph gradual domain adaptation (GGDA) framework, which constructs a compact domain sequence that minimizes information loss during adaptation. Our approach starts with an efficient generation of knowledge-preserving intermediate graphs over the Fused Gromov-Wasserstein (FGW) metric. A GGDA domain sequence is then constructed upon this bridging data pool through a novel vertex-based progression, which involves selecting “close” vertices and performing adaptive domain advancement to enhance inter-domain transferability. Theoretically, our framework provides implementable upper and lower bounds for the intractable inter-domain Wasserstein distance, \(W_{p}(\mu_{t},\mu_{t+1})\) , enabling its flexible adjustment for optimal domain formation. Extensive experiments across diverse transfer scenarios demonstrate the superior performance of our GGDA framework. Our code is available at https://github.com/Louisa-LPI/GGDA .
DOI: 10.1145/3815185
Authors: Pui Ieng Lei, Ximing Chen, Yijun Sheng, Yanyan Liu, Zhiguo Gong, Qiang Yang
Institutions: University of Macau, Hong Kong Polytechnic University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4406975435