Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 212-218.doi: 10.11896/jsjkx.201000015

• Database & Big Data & Data Science • Previous Articles     Next Articles

Deep Network Representation Learning Method on Incomplete Information Networks

FU Kun1, ZHAO Xiao-meng1, FU Zi-tong2, GAO Jin-hui1, MA Hao-ran3   

  1. 1 School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China
    2 School of Computer Sciences and Technology,Changchun University of Science and Technology,Changchun 130022,China
    3 Wuhan National Laboratory for Optoelectronics,Huazhong University of Science and Technology,Wuhan 430074,China
  • Received:2020-09-30 Revised:2021-03-02 Online:2021-12-15 Published:2021-11-26
  • About author:FU Kun,born in 1979,Ph.D,associate professor.Her main research interests include social network analysis and network representation learning.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(61806072).

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Abstract: The goal of network representation learning(NRL) is embedding network nodes into low-dimensional vector space,for effective feature representation of the downstream tasks.Due to the difficulty of information collection in the real-world scene-ries,large-scale networks often meet missing links between nodes.However,the most existing NRL models are designed on the foundation of complete information networks and that causes the poor robustness in incomplete networks.To solve this problem,a deep network representation learning(DNRL) method based on incomplete information networks is proposed.Firstly,a transfer probability matrix is used to dynamically mix the structural information and attribute information to cover the excessive loss caused by incomplete structural information.Then,a deep generative model variational autoencoder with powerful feature extraction capability is used to learn low-dimensional representation of nodes,and capture the potential high nonlinear features of nodes.Compared with the commonly used network representation learning methods,the experimental results on three real attri-bute networks show that the proposed model obviously improve effect in the node classification task with different degrees of link missing,visualization results clearly demonstrate the cluster relationship of nodes.

Key words: Attribute network, Incomplete information, Network representation learning, Variational autoencoder

CLC Number: 

  • TP391
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