An intuitionistic fuzzy method for social network prediction problems

Issue title: Some highlights on fuzzy systems and data mining

Guest editors: Shilei Sun, Silviu Ionita, Eva Volná, Andrey Gavrilov and Feng Liu

Article type: Research Article

Authors: Yang, Liming^a | Zhang, Wei^{a; b; *} | Liu, Wenqing^a | Chen, Yunfang^a

Affiliations: [a] College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, China | [b] Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, China

Correspondence: [*] Correspondence to: Wei Zhang, Tel.: +86 25 85866429; Fax: +86 2585866433; E-mail: zhangw@njupt.edu.cn.

Abstract: Social network evolution is a major component of social network analysis. Due to ubiquitous real world uncertainty, traditional deterministic networks tend to yield analytical deviations. To lessen this bias, an intuitionistic fuzzy based method is used herein to analyze the inner-structure transformations in social networks. In addition, a conception observation community is introduced into the proposed method in order to reduce the computational load. The network density and centrality metrics are used to obtain the intuitionistic fuzzy metrics of the micro relationships. Then, the social network development trends are predicted by analyzing the changes in the metrics using a Markov chain model. The experimental results demonstrate that the proposed intuitionistic fuzzy method is advantageous to the general methods.

Keywords: Social network prediction, intuitionistic fuzzy, observation community, Markov chain

DOI: 10.3233/JIFS-169199

Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 3131-3142, 2016