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Graph contrastive high-order structure representation learning
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DOI：10.1016/j.neucom.2025.132566.png)
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Abstract
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• A graph high-order structure capture module (SCM) is proposed, which combines node features and edge connections first. • The graph high-order structure capture module is combined with graph contrast learning to fully explore the graph structure. • Comprehensive experimental verification and result analysis are conducted on public benchmark datasets.
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