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Author

Xin Cao

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#graph neural networks Book Open access Nov 2026

Effective Heterophilous Heterogeneous Graph Learning via Decoupled Spectral Filtering and Curriculum Learning

Heterogeneous graphs (HGs) are widely used to model complex relational data in domains such as traffic networks, biology, and recommendation systems. While heterogeneous graph neural networks (HGNNs) demonstrate strong performance on homophilous benchmarks, they struggle to generalize to heterophilous HGs where connect...

Si-Qing Li, Yong-Min Shin, Won-Yong Shin et al. · 0 citations
Book Open access Aug 2026

Conflicting Node Discrimination Graph Neural Network for Semi-supervised Node Classification

Graph Neural Networks (GNNs) have exhibited extraordinary performance in data mining. The success depends on the message passing mechanism, which is consistent with the homophily assumption that adjacent nodes share similar behaviors. However, the connected nodes in real-world graphs often show dissimilar behaviors, wh...

Wenjun Wang, Xin Cao, Yawen Li et al. · 0 citations

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