Signed graph embedding via multi-order neighborhood feature fusion and contrastive learning

Signed graphs have been widely applied to model real-world complex networks with positive and negative links, and signed graph embedding has become a popular topic in the field of signed graph analysis. Although various signed graph embedding methods have been proposed, most of them still suffer fro...

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Veröffentlicht in:	Neural networks 2025-02, Vol.182, p.106897, Article 106897
Hauptverfasser:	He, Chaobo, Cheng, Hao, Yang, Jiaqi, Tang, Yong, Guan, Quanlong
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Contrastive learning Graph convolutional networks Humans Machine Learning Multi-order neighborhood feature Neural Networks, Computer Signed graph embedding Structural balance theory
Online-Zugang:	Volltext
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