COLA: Complementary Context Retrieval with Utility Calibration for Graph Foundation Model Adaptation

This paper studies the problem of few-shot adaptation for graph foundation models (GFMs), which aims to transfer pre-trained GFMs to unseen domains with limited support sets. Previous approaches usually introduce learnable task-aware prompts to adapt GFMs to new domains. Despite the progress, these approaches primarily exploit parametric knowledge in pre-trained models while neglecting rich contextual knowledge embedded in source graphs. Towards this end, we propose a novel approach named Complementary Context Retrieval with Utility Calibration (COLA) for graph foundation model adaptation. The core of our COLA is to retrieve relevant graph candidates from complementary knowledge sources and then refine them into fine-grained contexts guided by their estimated utility for graph representation adaptation. In particular, we first build a graph semantics database and a graph structure database from source domains to capture complementary knowledge. Given a target query, we retrieve relevant graph candidates from these two databases using graph representations and graph kernels, respectively. More importantly, we leverage reinforcement learning with pseudo-target calibration to train a lightweight policy model, which estimates the utility of retrieved graphs to guide fine-grained context selection from abundant candidates. The refined complementary contexts are then incorporated into target graph representations under the guidance of null-aware routing for domain adaptation. Extensive experiments on benchmark datasets validate the effectiveness of the proposed COLA in comparison with extensive baselines. Our code is available at https://anonymous.4open.science/r/COLA-D0DC.