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arXiv · 2610.02795

Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

Yue Hou·Ruomei Liu·Yingke Su·Junran Wu·Ke Xu
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Abstract

Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution drifts continually over time. Prevailing solutions rely on training auxiliary generative modules to synthesize memory graphs for cross-domain adaptation, which incurs substantial computational overhead and scales poorly under prolonged distribution shifts. We argue that a more economical path exists: rather than generating memory, one can crystallize it. To this end, we propose Efficient Memory Crystallization (EMC), a training-free test-time framework that distills each incoming graph domain into a compact, semantically faithful memory through a closed-form solution to a memory-oriented distribution-matching objective, thereby eliminating redundant domain information under continual covariate shifts. To preserve both generalizability and adaptability as the model traverses a long sequence of target domains, EMC further models inter-domain dependencies through state-evolving memories and admits a theoretically grounded, tighter generalization error bound than direct adaptation. Extensive experiments demonstrate the superior performance of EMC over state-of-the-art baselines on graphs under non-stationary distribution shifts, while reducing average runtime by 87.4% and GPU memory consumption by 92.4% relative to the recent competitor, making continual graph adaptation practical at scale.

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Yue Hou, Ruomei Liu, Yingke Su, Junran Wu, Ke Xu. 2026-10-02. Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts. https://arxiv.org/abs/2610.02795

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