On community detection in complex networks based on different training algorithms: A case study on prediction of depression of internet addiction

Jovana Cvetković and Milan Cvetković

Physica A: Statistical Mechanics and its Applications, 2019, vol. 523, issue C, 1161-1170

Abstract: Community structure is an important feature of complex networks. In recent years, community detection algorithms based on optimization has been of interest for many researchers. One way to detect these communities is the use of algorithms based on swarm intelligence to find the optimal solution. Cuckoo optimization is discussed, and a new objective function is presented. The proposed method tries to maximize network modularity function and the similarity of nodes to each other at the same time. It also seeks to provide a better equation to calculate the similarity of nodes in a complex network. New objective function has raised the speed of convergence to the optimal solution and provides a solution with better quality. The results of simulations conducted on a real network data set show that the proposed method discovers communities with acceptable and efficient quality. The proposed methods are tested for prediction of depression of internet addiction and corresponding results are observed.

Keywords: Community detection; Complex networks; Cuckoo optimization; Optimization algorithms; Depression of internet addiction (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:523:y:2019:i:c:p:1161-1170

DOI: 10.1016/j.physa.2019.03.102

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