ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (5): 954-970.doi: 10.7544/issn1000-1239.2020.20190331

• 人工智能 • 上一篇    下一篇

融合多元信息的多关系社交网络节点重要性研究

罗浩1,闫光辉1,张萌1,包峻波1,李俊成1,刘婷1,杨波2,魏军2   

  1. 1( 兰州交通大学电子与信息工程学院 兰州 730070);2( 国网甘肃省电力公司信通公司 兰州 730050) (luoh382@163.com)
  • 出版日期: 2020-05-01
  • 基金资助: 
    国家自然科学基金项目(61662066,61163010);甘肃省青年科技基金计划项目(1606RJYA222)

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).

摘要: 识别重要节点是社会网络分析领域的重要任务之一,也是理解复杂网络结构和动力学特性的有效方式,迄今发展起来的节点重要性分析框架主要面向单关系网络.多关系网络作为准确刻画现实世界复杂系统的典型建模形式,已成为当前网络科学领域研究的热点,但对于多关系网络的节点重要性研究尚缺乏系统性的研究成果.针对多关系社交网络节点重要性研究问题,通过构建有向多重网络模型和基于张量代数的数学框架对其进行建模和分析,将中心性、声望和传递性作为影响社交网络节点重要性的关键因素,提出了一种面向多关系社交网络的节点重要性度量指标,并针对其存在不足引入D-S(Dempster-Shafer)证据理论进行改进,进一步提出了IOMEC(in-degree out-degree multiplex evidential centrality)节点重要性度量方法.在4个真实网络上的实验结果表明:采取信息融合的方法可以有效消除多关系网络耦合信息和传递机制对节点重要性评测造成的影响,提出的IOMEC方法能够更准确地对节点重要性进行度量,并且具有较低的时间复杂度,在论证节点中心性和声望是衡量节点重要程度主要因素的同时,说明了综合考虑节点传递性的必要性.所做工作为多关系网络节点重要性研究提供新的思路方法的同时,进一步拓展了信息融合技术的应用场景.

关键词: 多关系网络, 社交网络, 节点重要性, 中心性, 声望, 传递性, 信息融合, D-S证据理论

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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