An improved multi-view attention network inspired by coupled P system for node classification

Most of the existing graph embedding methods are used to describe the single view network and solve the single relation in the network. However, the real world is made up of networks with multiple views of complex relationships, and the existing methods can no longer meet the needs of people. To sol...

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Bibliographic Details
Published in:PloS one 2022-04, Vol.17 (4), p.e0267565
Main Authors: Liu, Qian, Liu, Xiyu
Format: Article
Language:English
Subjects:
Algorithms
Biocomputers
Biology and Life Sciences
Cell division
Classification
Collaboration
Computer and Information Sciences
Computer applications
Efficiency
Embedding
Evaluation
Fault diagnosis
Humans
Information processing
Learning
Medical research
Medicine, Experimental
Membranes
Neural networks
P system
Physical Sciences
Research and Analysis Methods
Social Sciences
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Summary:Most of the existing graph embedding methods are used to describe the single view network and solve the single relation in the network. However, the real world is made up of networks with multiple views of complex relationships, and the existing methods can no longer meet the needs of people. To solve this problem, we propose a novel multi-view attention network inspired by coupled P system(MVAN-CP) to deal with node classification. More specifically, we design a multi-view attention network to extract abundant information from multiple views in the network and obtain a learning representation for each view. To enable the views to collaborate, we further apply attention mechanism to facilitate the view fusion process. Taking advantage of the maximum parallelism of P system, the process of learning and fusion will be realized in the coupled P system, which greatly improves the computational efficiency. Experiments on real network data sets indicate that our model is effective.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0267565