Heterogeneous hypergraph representation learning for link prediction

Heterogeneous graph representation learning gains popularity due to its powerful capabilities of feature extraction and numerous related algorithms have emerged for various downstream tasks in graph structural datasets. However, the interactions among nodes for the heterogeneous graphs in the real w...

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Veröffentlicht in:	The European physical journal. B, Condensed matter physics Condensed matter physics, 2024-10, Vol.97 (10), Article 153
Hauptverfasser:	Zhao, Zijuan, Yang, Kai, Guo, Jinli
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Complex Systems Condensed Matter Physics Datasets Feature extraction Fluid- and Aerodynamics Graph neural networks Graph representations Graph theory Graphical representations Graphs Machine learning Network analysis Nodes Physics Physics and Astronomy Regular Article - Statistical and Nonlinear Physics Solid State Physics
Online-Zugang:	Volltext
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