Gradual Domain Adaptation for Graph Learning

• URL: http://arxiv.org/abs/2501.17443v3
• Date: Thu, 28 Aug 2025 11:01:38 GMT
• Title: Gradual Domain Adaptation for Graph Learning
• Authors: Pui Ieng Lei, Ximing Chen, Yijun Sheng, Yanyan Liu, Zhiguo Gong, Qiang Yang,
• Abstract summary: We present a graph gradual domain adaptation (GGDA) framework, which constructs a compact domain sequence that minimizes information loss during adaptation.<n>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.
• Score: 15.648578809740414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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