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Correlations multiplexing for link prediction in multidimensional network spaces

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In social networks, link establishment among users is affected by diversity correlations. In this paper, we study the formation of links, map correlations into multidimensional network spaces and apply their behavioral and structural features to the problem of link prediction. First, by exploring user behavioral correlation and network structural correlation, we map them into three network spaces: following space, interaction space and structure space. With a hierarchical process, the coupling relationship between the spaces can be reduced and we can analyze the correlation in each space separately. Second, by taking advantage of the latent Dirichlet allocation (LDA) topic model for dealing with the polysemy and synonym problems, the traditional text modeling method is improved by Gaussian weighting and applied to user behavior modeling. In this way, the expression ability of topics can be enhanced, and improved topic distribution of user behavior can be obtained to mine user correlations in the following space and the interaction space. Moreover, the method can be extended using the hidden naive Bayesian algorithm which is good at reducing attribute independence. By quantifying the dependencies between common neighbors, we can analyze user correlations in the structure space and multiplex the correlations of the other two spaces to link prediction. The experimental results indicate that the method can effectively improve link prediction performance.

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This work was supported by National Basic Research Program of China (Grant No. 2013CB329606), National Natural Science Foundation of China (Grant No. 61772098), Chongqing Science and Technology Commission Project (Grant No. cstc2017jcyjAX0099), and Foundation of Ministry of Education of China and China Mobile (Grant No. MCM20130351).

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Correspondence to Yunpeng Xiao.

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Xiao, Y., Li, X., Liu, Y. et al. Correlations multiplexing for link prediction in multidimensional network spaces. Sci. China Inf. Sci. 61, 112103 (2018). https://doi.org/10.1007/s11432-017-9334-3

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  • social networks
  • multidimensional network spaces
  • link prediction
  • LDA
  • hidden naive Bayes