The rapid growth of graph-structured data has led to the development of the GC-SNTK method, which efficiently condenses large graphs while maintaining predictive accuracy. By reformulating the problem as a KRR task and incorporating SNTK, the proposed method outperforms traditional approaches in terms of both performance and efficiency.
Existing efforts in graph condensation have faced challenges such as computational costs and unstable training. The proposed GC-SNTK method addresses these issues by leveraging KRR and SNTK to streamline the process. Through extensive experiments on various datasets, the effectiveness and efficiency of GC-SNTK are demonstrated.
GC-SNTK introduces a novel framework that significantly improves graph condensation efficiency by replacing iterative GNN training with KRR. By capturing topological signals using SNTK, the method offers powerful generalization capabilities across different GNN architectures.
The computational complexity analysis shows that GC-SNTK is more time-efficient than traditional methods like GCond, especially at smaller condensation scales. The experimental results validate the effectiveness of GC-SNTK in reducing dataset size while maintaining high prediction performance.
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by Lin Wang,Wen... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2310.11046.pdfDeeper Inquiries