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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Bibliographic Details
Published in:Neural computing & applications Vol. 35; no. 4; pp. 3357 - 3372
Main Authors: Jia, Xiangen, Jiang, Min, Dong, Yihong, Zhu, Feng, Lin, Haocai, Xin, Yu, Chen, Huahui
Format: Journal Article
Language:English
Published: London Springer London 01.02.2023
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07862-6