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International Journal of Engineering، جلد ۳۶، شماره ۱۱، صفحات ۲۰۵۲-۲۰۶۲
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عنوان انگلیسی A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory
چکیده انگلیسی مقاله Today, with the proliferation of complex networks and their large amounts of data, researchers have great concerns about the accurate community detection methods. The difficulty in analyzing these networks stems from their enormous size and the complex relationships among the members of the networks. It is difficult to analyze the deep relationships and mechanisms by just looking at the whole. Traditional methods have some problems and limitations when analyzing these networks such as feature extraction, high reliance on the initial phase settings, computational complexity, neglect of network relationships and content. From the perspective of relationships and interactions between individuals, the environment of complex networks can be compared to a game in which nodes acting as players or agents may join or leave a community based on similar structural or semantic characteristics. Consequently, there is a strong tendency to use cooperative and non-cooperative games to detect communities. Moreover, the amalgamation of deep learning techniques and game theory has recently been proven to be highly effective in extracting communities. Deep learning techniques have demonstrated enhanced capability in feature engineering and automate the process. In this study, the authors make effort to detect rational and accurate communities based on structural and content features with the help of traditional approaches, deep learning, as well as cooperative and non-cooperative games. The efficiency of this study is demonstrated by experimental findings on real datasets, and confirming that it is able enough to identify those communities that are more meaningful.
کلیدواژه‌های انگلیسی مقاله Complex Networks Community Detection Deep Learning Cooperative Game Non,cooperative game
نویسندگان مقاله A. Torkaman |
Department of Computer, South Tehran Branch, Islamic Azad University, Tehran, Iran

K. Badie |
E-Services and E-Content Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran

A. Salajegheh |
Department of Computer, South Tehran Branch, Islamic Azad University, Tehran, Iran

M. H. Bokaei |
Department of Information Technology, ICT Research Institute, Tehran, Iran

S. F. Fatemi Ardestani |
Sharif University of Technology, Tehran, Iran

نشانی اینترنتی https://www.ije.ir/article_176170_c238b67f3a4231ab054a1a423c9a79a0.pdf
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