Collaborative Knowledge Extraction and Integration for Graph Domain Incremental Learning

Graph Domain Incremental Learning (GDIL) aims to acquire knowledge from a continuous stream of graph domains while mitigating catastrophic forgetting. While parameter-isolation methods leveraging graph parameter-efficient adaptation show promise, prompt-based techniques struggle to adapt to GDIL, and low-rank adaptation methods based on a shared classification layer lead to knowledge confusion.Our empirical observations reveal that transferable knowledge is primarily concentrated in the representation layer. Further, we argue that domain-agnostic representations that are not tied to the classification characteristics are needed to assist the new model in capturing more discriminative features for graph domain incremental learning.Motivated by these insights, we propose COllabOrative Knowledge Extraction and integRation (COOKER) method for GDIL to mine inter-domain relationships and uncover the potential of domain-agnostic representations. Specifically, COOKER employs domain-specific LoRA modules and classifiers to capture specific knowledge. A domain-agnostic LoRA module is instantiated to extract transferable knowledge through contrastive acquisition and topology alignment. We introduce collaborative dynamic integration of dual representations to enable adaptive integration, guided by a complementarity loss to eliminate information redundancy. Extensive experiments demonstrate that COOKER significantly outperforms existing baselines, achieving up to a 4.7% improvement in average performance.