Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 13 ، دوره 7، شماره 1 ، فروردین 2019، صفحه 123-133 1.08 M )
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2020.6145.283 نویسندگان
S.M. Nematollahzadeh 1 ، 2 ؛ S. Ozgoli* 2 M. Sayad Haghighi 3 1 Iran Telecommunication Research Center, Tehran, Iran and Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran2 Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran3 School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran and School of Electrical Engineering and Computer Science, Queensland University of Technology, Queensland , Australia
تاریخ دریافت : 08 فروردین 1397 ،
تاریخ بازنگری : 14 تیر 1397 ،
تاریخ پذیرش : 17 آبان 1397
چکیده Background and Objectives: One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental data in order to identify the influence network between individuals in social networks.Methods: The employed method is based on convex optimization and can identify interaction patterns precisely. This technique considers individuals’ opinions in multiple dimensions. Moreover, the opinion dynamics models that have been introduced in the literature are investigated. Then, the three models which are the most comprehensive and vastly accepted in the literature, are considered. These three models are then proven to satisfy the convexity condition, which means they can be used for the introduced method of identification.Results: Four real experiments have been conducted in this research that their results verify the application of our method. The outcomes of these experiments are presented in this paper.Conclusion: کلیدواژهها
Convex optimization ؛ Identification algorithms ؛ Digital filters ؛ Opinion dynamics ؛ Social networks
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