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Multi-view network embedding with node similarity ensemble

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Abstract

Node similarity is utilized as the most popular guidance for network embedding: nodes more similar in a network should still be more similar when mapping node information from a high-dimensional vector space to a low-dimensional vector space. Most existing methods preserve a single node similarity in the network embedding, which can merely preserve one-side network structural information. Though some works try to utilize several node similarities to preserve more network information, they fail to consider the interrelationships between the latent spaces preserving different node similarities. This causes both network information insufficiency and network information redundancy. To solve the problems of existing works, we propose a novel multi-view network embedding model with node similarity ensemble. Node similarities are first selected to maximize the represented network information while minimizing the information redundancy. For each combination of the selected node similarities, a latent space is generated as a view of the network. A Canonical Correlation Analysis based approach is then used to extract the common structure of the latent spaces alignment, and a neural network based approach is used to extract the view-specific latent structure by measuring the asymmetric KL divergence of nodes’ Gaussian distribution. The common structure and the view-specific structure of multiple views are merged to perverse the overall network information. Experiments held on the real-world networks verified the superiority of the proposed method to existing works.

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Notes

  1. https://linqs.soe.ucsc.edu/data

  2. https://github.com/thunlp/openne

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Acknowledgements

This research was supported by National Natural Science Foundation of China (Grant No. 61672284), Natural Science Foundation of Jiangsu Province (Grant No. BK20171418), China Postdoctoral Science Foundation (Grant No. 2016 M591841), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1601225C). The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no. RGP-VPP-264.

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Authors and Affiliations

  1. College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Weiwei Yuan, Kangya He, Chenyang Shi & Donghai Guan

  2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China

    Weiwei Yuan

  3. School of Computer Engineering, Nanjing Institute of Technology, Nanjing, China

    Yuan Tian

  4. Department of Computer Science, King Saud University, Riyadh, Saudi Arabia

    Abdullah Al-Dhelaan & Mohammed Al-Dhelaan

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  1. Weiwei Yuan
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  2. Kangya He
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  3. Chenyang Shi
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  4. Donghai Guan
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  5. Yuan Tian
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  6. Abdullah Al-Dhelaan
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  7. Mohammed Al-Dhelaan
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Corresponding author

Correspondence to Donghai Guan.

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This article belongs to the Topical Collection: Special Issue on Application-Driven Knowledge Acquisition

Guest Editors: Xue Li, Sen Wang, and Bohan Li

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Yuan, W., He, K., Shi, C. et al. Multi-view network embedding with node similarity ensemble. World Wide Web 23, 2699–2714 (2020). https://doi.org/10.1007/s11280-020-00799-7

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