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|a 10.1109/TPAMI.2022.3182052
|2 doi
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|a pubmed24n1140.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Gao, Yue
|e verfasserin
|4 aut
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| 245 |
1 |
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|a HGNN+
|b General Hypergraph Neural Networks
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 07.04.2023
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|a Date Revised 11.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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 recently been proposed to address the problem of multi-modal/multi-type data correlation by directly concatenating the hypergraphs constructed from each single individual modality/type, which is difficult to learn an adaptive weight for each modality/type. In this paper, we extend the original conference version HGNN, and introduce a general high-order multi-modal/multi-type data correlation modeling framework called HGNN + to learn an optimal representation in a single hypergraph based framework. It is achieved by bridging multi-modal/multi-type data and hyperedge with hyperedge groups. Specifically, in our method, hyperedge groups are first constructed to represent latent high-order correlations in each specific modality/type with explicit or implicit graph structures. An adaptive hyperedge group fusion strategy is then used to effectively fuse the correlations from different modalities/types in a unified hypergraph. After that a new hypergraph convolution scheme performed in spatial domain is used to learn a general data representation for various tasks. We have evaluated this framework on several popular datasets and compared it with recent state-of-the-art methods. The comprehensive evaluations indicate that the proposed HGNN + framework can consistently outperform existing methods with a significant margin, especially when modeling implicit data correlations. We also release a toolbox called THU-DeepHypergraph for the proposed framework, which can be used for various of applications, such as data classification, retrieval and recommendation
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|a Journal Article
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| 700 |
1 |
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|a Feng, Yifan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ji, Shuyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ji, Rongrong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 3 vom: 14. März, Seite 3181-3199
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:45
|g year:2023
|g number:3
|g day:14
|g month:03
|g pages:3181-3199
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2022.3182052
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