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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Podrobná bibliografie
Vydáno v:	Neural computing & applications Ročník 35; číslo 4; s. 3357 - 3372
Hlavní autoři:	Jia, Xiangen, Jiang, Min, Dong, Yihong, Zhu, Feng, Lin, Haocai, Xin, Yu, Chen, Huahui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.02.2023 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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 heterogeneous graphs. However, most existed methods can only handle unimodal heterogeneous graphs. Moreover, most existing heterogeneous graph mining methods are based on meta-paths that depend on domain experts for modeling. In this paper, we propose a novel multimodal heterogeneous graph attention network (MHGAT) to address these problems. Specifically, we exploit edge-level aggregation to capture graph heterogeneity information to achieve more informative representations adaptively. Further, we use the modality-level attention mechanism to obtain multimodal fusion information. Because plain graph convolutional networks can not capture higher-order neighborhood information, we utilize the residual connection and the dense connection access to obtain it. Extensive experimental results show that the MHGAT outperforms state-of-the-art baselines on three datasets for node classification, clustering, and visualization tasks.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0941-0643 1433-3058
DOI:	10.1007/s00521-022-07862-6