This paper investigates a novel and critical problem of Graph Continual Test-Time Training, which aims to enable a frozen pre-trained graph model to adapt continuously to evolving out-of-distribution (OOD) graphs without supervision. Existing test-time training methods primarily focus on one-step adaptation and overlook long-term knowledge retention, while conventional continual learning approaches rely on labeled data and static memory replay. Consequently, they are unable to handle sequential OOD domains effectively, often suffering from severe forgetting and limited efficiency in dynamic graph environments. To address these challenges, we propose DPCGL (Dynamic Prompts-based Continual Graph Learning), a data-centric framework that performs continual test-time training through adaptive prompt optimization. DPCGL freezes the pre-trained backbone and maintains a dynamic prompt pool, where prompts are adaptively selected and updated for each incoming graph domain. This design enables parameter-efficient adaptation and mitigates forgetting by organizing transferable knowledge within prompts rather than model weights. Furthermore, DPCGL jointly optimizes three objectives: similarity alignment for representativeness, KL divergence regularization for knowledge preservation, and diversity constraint for generalization, providing both stability and adaptability during continual adaptation. Extensive experiments on multiple evolving OOD graph benchmarks demonstrate that DPCGL achieves state-of-the-art performance, effectively alleviating catastrophic forgetting and enabling robust continual adaptation across domains.
Cai et al. (Thu,) studied this question.