Journal on Communications ›› 2022, Vol. 43 ›› Issue (5): 214-225.doi: 10.11959/j.issn.1000-436x.2022095

• Correspondences • Previous Articles    

Research on HMM based link prediction method in heterogeneous network

Rong QIAN1,2, Jianting XU2, Kejun ZHANG1,2, Hongyu DONG1, Fangyuan XING1   

  1. 1 Department of Cyberspace Security, Beijing Electronic Science and Technology Institute, Beijing 100070, China
    2 College of Computer Science and Technology, Xidian University, Xi’an 710071, China
  • Revised:2022-04-07 Online:2022-05-25 Published:2022-05-01
  • Supported by:
    The National Key Research and Development Program of China(2018YFB1004101)

Abstract:

In order to solve the problem that incomplete mining of structural information and semantic information in heterogeneous networks, a link prediction method combining meta-path-based analysis and hidden Markov model was proposed for link prediction of heterogeneous network.Considering that clustering could effectively capture the structural information of heterogeneous network, the k-means algorithm was improved to obtain the initial clustering center method based on the minimum distance mean square error, and it was applied to the hidden Markov model, first-order cluster hidden markov model (C-HMM(1)) link prediction method, and a link prediction method for heterogeneous network with second-order cluster hidden Markov model (C-HMM(2)) were designed.Further, considering the feature information of the data, a link prediction method called ME-HMM that combined the maximum entropy model and the second-order Markov model was proposed.The experimental results show that the ME-HMM has higher link prediction accuracy than the C-HMM, and the ME-HMM method has better performance than the C-HMM method because it fully considers the feature information of the data.

Key words: heterogeneous network, link prediction, hidden Markov model, clustering, maximum entropy

CLC Number: 

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