HGNN+: General Hypergraph Neural Networks

Graph Neural Networks have attracted increasing attention in recent years. However, existing GNN frameworks are deployed based upon simple graphs, which limits their applications in dealing with complex data correlation of multi-modal/multi-type data in practice. A few hypergraph-based methods have...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 45; H. 3; S. 3181 - 3199
Hauptverfasser:	Gao, Yue, Feng, Yifan, Ji, Shuyi, Ji, Rongrong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	classification Convolution Correlation Data correlation Data models Evaluation Graph neural networks Graph theory Hypergraph hypergraph convolution Mathematical models Modelling Neural networks Representation learning Representations Smart structures Social networking (online) Task analysis
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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