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[期刊论文]

Multi-view heterogeneous graph learning with compressed hypergraph neural networks

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author:

Huang, A. (Huang, A..) [1] | Fang, Z. (Fang, Z..) [2] | Wu, Z. (Wu, Z..) [3] | Unfold

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Scopus

Abstract:

Multi-view learning is an emerging field of multi-modal fusion, which involves representing a single instance using multiple heterogeneous features to improve compatibility prediction. However, existing graph-based multi-view learning approaches are implemented on homogeneous assumptions and pairwise relationships, which may not adequately capture the complex interactions among real-world instances. In this paper, we design a compressed hypergraph neural network from the perspective of multi-view heterogeneous graph learning. This approach effectively captures rich multi-view heterogeneous semantic information, incorporating a hypergraph structure that simultaneously enables the exploration of higher-order correlations between samples in multi-view scenarios. Specifically, we introduce efficient hypergraph convolutional networks based on an explainable regularizer-centered optimization framework. Additionally, a low-rank approximation is adopted as hypergraphs to reformat the initial complex multi-view heterogeneous graph. Extensive experiments compared with several advanced node classification methods and multi-view classification methods have demonstrated the feasibility and effectiveness of the proposed method. © 2024 Elsevier Ltd

Keyword:

Graph neural network Heterogeneous graph Hypergraph convolution Multi-view learning

Community:

  • [ 1 ] [Huang A.]School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
  • [ 2 ] [Fang Z.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
  • [ 3 ] [Wu Z.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
  • [ 4 ] [Tan Y.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
  • [ 5 ] [Han P.]School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
  • [ 6 ] [Wang S.]College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
  • [ 7 ] [Zhang L.]School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

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Source :

Neural Networks

ISSN: 0893-6080

Year: 2024

Volume: 179

6 . 0 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

30 Days PV: 2

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