Multimodal heterogeneous graph attention network

The real world involves many graphs and networks that are essentially heterogeneous, in which various types of relations connect multiple types of vertices. With the development of information networks, node features can be described by data of different modalities, resulting in multimodal heterogen...

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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 4; S. 3357 - 3372
Hauptverfasser:	Jia, Xiangen, Jiang, Min, Dong, Yihong, Zhu, Feng, Lin, Haocai, Xin, Yu, Chen, Huahui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.02.2023 Springer Nature B.V
Schlagworte:	Actors Apexes Artificial Intelligence Classification Clustering Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Deep learning Graph representations Graphs Heterogeneity Image Processing and Computer Vision Neighborhoods Networks Neural networks Original Article Probability and Statistics in Computer Science Semantics Subject specialists Visualization Network representation learning Heterogeneous networks Graph convolutional networks Multimodal Clustering
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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