A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

Abstract

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.