An Extreme Learning Machine-Based Community Detection Algorithm in Complex Networks. (6th August 2018)

• Record Type: Journal Article
• Authors: Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing
• Other Names: Kamal Shyam Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-08-06
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/8098325
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Community structure, one of the most popular properties in complex networks, has long been a cornerstone in the advance of various scientific branches. Over the past few years, a number of tools have been used in the development of community detection algorithms. In this paper, by means of fusing unsupervised extreme learning machines and the k -means clustering techniques, we propose a novel community detection method that surpasses traditional k -means approaches in terms of precision and stability while adding very few extra computational costs. Furthermore, results of extensive experiments undertaken on computer-generated networks and real-world datasets illustrate acceptable performances of the introduced algorithm in comparison with other typical community detection algorithms.