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StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
April 23, 2024, 4:44 a.m. | Shengzhong Zhang, Wenjie Yang, Xinyuan Cao, Hongwei Zhang, Zengfeng Huang
cs.LG updates on arXiv.org arxiv.org
Abstract: Graph contrastive learning (GCL) has become a powerful tool for learning graph data, but its scalability remains a significant challenge. In this work, we propose a simple yet effective training framework called Structural Compression (StructComp) to address this issue. Inspired by a sparse low-rank approximation on the diffusion matrix, StructComp trains the encoder with the compressed nodes. This allows the encoder not to perform any message passing during the training stage, and significantly reduces the …
abstract arxiv become challenge compression cs.lg data framework graph graph data issue low propagation scalability simple tool training type work
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