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

摘要

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. (C) 2019 Elsevier B.V. All rights reserved.