Abstract
Detecting the communities that exist within complex social networks has a wide range of application in business, engineering, and sociopolitical settings. As a result, many community detection methods are being developed by researchers in the academic community. If the communities within social networks can be more accurately detected, the behavior or characteristics of each community within the networks can be better understood, which implies that better decisions can be made. In this paper, a discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods. The comparative study shows that the unconscious search algorithm consistently produced the highest modularity that was discovered through the comprehensive review of the literature.
Highlights
The amount of research dedicated to complex networks has increased over the last decades due to its wide range of applications
A discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods
Given the popularity of these benchmark networks, the results obtained from the unconscious search (US) algorithm were compared to the results published by authors exploring community detection methods
Summary
The amount of research dedicated to complex networks has increased over the last decades due to its wide range of applications. By researching complex networks, the research community has investigated various phenomena within economic, social, and technological settings (Chen & Redner, 2010). Within these problem areas, social structures can be represented as a complex network. Social networks consist of a set of communities, which are sometimes referred to as called clusters or modules. This particular area of study is receiving more attention from the research community. Internet social networks (Lambiotte & Ausloos, 2005), scientific reference networks (Chen & Redner, 2010), biological networks (Diao, Li, Feng, Yin, & Pan, 2007), neurological networks (Schwarz, Gozzi, & Bifone, 2008), epidemiology networks (Sun & Gao, 2007), transportation networks (Barigozzi, Fagiolo, & Mangioni, 2011), and even political networks (Porter, Mucha, Newman, & Friend, 2007) are some of the areas being investigated by practitioners and researchers
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