Continual-GraphLLM: Dynamic Graph Large Language Model with Invariance Regularized Adaptive Multi-Scale Experts

Abstract

Dynamic text-attributed graphs (DyTAGs) exhibit coupled textual and structural dynamics, and existing mainstream approaches for DyTAGs extend conventional large language models (LLMs) to capture both dynamics, thereby giving rise to dynamic graph LLMs. However, in DyTAGs, the continuous emergence of new nodes and edges with incoming textual content and interactions drives the joint evolution of graph structural-textual patterns, causing existing methods to struggle with evolving patterns. This motivates a largely unexplored problem of continual learning on DyTAGs, which aims to adapt to constantly evolving graph structural-textual patterns while retaining past knowledge, which imposes two challenges: 1) unlike common graphs, graph structure and textual semantics in emerging DyTAG patterns jointly evolve, requiring dynamic graph LLMs to adapt structure, text, and graph-text fusion simultaneously; and 2) updating dynamic graph LLMs to fit a new pattern may destroy the global graph-text fusion capabilities and bias the model towards recent local dynamics. To address these challenges, we propose a novel Continual Learning Dynamic Graph LLM framework (Continual-GraphLLM) to continually adapt to incoming patterns by routing them to experts specialized in similar past patterns, while mitigating the overwriting of previously learned patterns by assigning new experts to unseen patterns. Specifically, we propose a graph-text factor-based router to adapt to incoming structural-textual joint patterns by utilizing latent factors to adaptively activate suitable experts. Furthermore, we design invariance regularized multi-scale experts that mitigate forgetting by capturing the invariances among learned patterns assigned to the same expert, where each expert progressively integrates structural and textual information from local scale to global scale. Extensive experiments on real-world DyTAGs demonstrate the superiority of our method over competitive baselines, highlighting its effectiveness in adapting to emerging DyTAG patterns.