Fast Graph Condensation with Structure-based Neural Tangent Kernel

Lin WANG, Wenqi Fan, Jiatong LI, Yao Ma, Qing Li

Keywords: Graph Condensation, Dataset Distillation, Graph Neural Networks, Kernel Ridge Regression, Neural Tangent Kernel

TL;DR: Fast condensing a large graph into a smaller one with few nodes, while retaining equivalent comparable expressivity to the original graph data.

Abstract: The rapid development of Internet technology has given rise to a vast amount of graph-structured data. Graph Neural Networks (GNNs), as an effective method for various graph mining tasks, incurs substantial computational resource costs when dealing with large-scale graph data. A data-centric manner solution is proposed to condense the large graph dataset into a smaller one without sacrificing the predictive performance of GNNs. However, existing efforts condense graph-structured data through a computational intensive bi-level optimization architecture also suffer from massive computation costs. In this paper, we propose reforming the graph condensation problem as a Kernel Ridge Regression (KRR) task instead of iteratively training GNNs in the inner loop of bi-level optimization. More specifically, We propose a novel dataset condensation framework (GC-SNTK) for graph-structured data, where a Structure-based Neural Tangent Kernel (SNTK) is developed to capture the topology of graph and serves as the kernel function in KRR paradigm. Comprehensive experiments demonstrate the effectiveness of our proposed model in accelerating graph condensation while maintaining high prediction performance.

Track: Web Mining and Content Analysis

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Submission Number: 2342