Progressive Stacking for Scalable Graph Condensation

摘要

Large-scale graph data has demonstrated significant success in graph representation learning, but the associated high computational cost and inefficiency hinder its widespread adoption across diverse applications. Graph condensation has emerged as a promising solution to reduce time and memory demands while preserving generalization performance comparable to the original graph. Although existing graph condensation methods have proven effective, they are constrained by their reliance on repeatedly optimizing a condensed graph at a fixed scale, which demands significant computational resources and lacks flexibility to accommodate varying training requirements. This motivates us to explore alternative approaches that incrementally refine and expand condensed graphs. To tackle above challenges in graph condensation, this paper presents Progressive Stacking (ProStack), a method that progressively constructs condensed graphs by incrementally expanding their structure and semantics. ProStack utilizes graph memory to retain global structural and feature information from the original graph, facilitating the reuse of essential features and patterns. The method employs a memory-guided dual optimization approach, focusing on structural consistency in the feature generation stage and optimizing the alignment of the label features in the label prediction stage. By stage-wise graph stacking, ProStack progressively refines the condensed graph, ensuring feature coherence and maintaining structural integrity throughout the process. Experimental results demonstrate that ProStack outperforms existing graph condensation techniques in efficiency and effectiveness.