ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (5): 954-970.doi: 10.7544/issn1000-1239.2020.20190331

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Research on Node Importance Fused Multi-Information for Multi-Relational Social Networks

Luo Hao1, Yan Guanghui1, Zhang Meng1, Bao Junbo1, Li Juncheng1, Liu Ting1, Yang Bo2, Wei Jun2   

  1. 1( School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070);2( Information and Telecommunication Company, State Grid Gansu Electric Power Company, Lanzhou 730050)
  • Online:2020-05-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61662066, 61163010) and the Technique Foundation Program for Young Scientists of Gansu Province (1606RJYA222).

Abstract: Identifying critical nodes is one of the principal tasks of social network analysis, and it is essential to understand the structure and dynamic characteristics of the complex networks. However, the analysis framework of node importance mainly focuses on single-relational networks. As a typical model of the real world, the multi-relational network has become one of the hot topics in the field of network science, in which the research on node importance lacks systematic research. Focusing on the study of node importance in multi-relational social networks, we create the directed multiplex network model to describe a multi-relational network and use the representation framework based on tensor algebra to analyze it. Meanwhile, we propose a measure of node importance considered the influence of centrality, prestige, transitivity in multi-relational social networks. Considering the influence of coupling information and the difference of transmission mechanism for node importance on multi-relational networks, in this work we improve the method and propose another more efficient method called IOMEC to evaluate the node importance. Experimental results on four real networks show that the method of information fusion can effectively eliminate the influence on node importance evaluation, which is caused by the coupling information and the transmission mechanism of the multi-relational network. The IOMEC method can measure the importance of nodes more accurately and has lower time complexity. The experimental results demonstrate that centrality and prestige are the main factors to evaluate the node importance and the necessity of considering the transitivity of nodes. In this work we not only provide new ideas and methods for evaluating node importance for multi-relational networks but also expand the application of information fusion technology.

Key words: multi-relational network, social network, node importance, centrality, prestige, transitivity, information fusion, Dempster-Shafer theory of evidence

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