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Complexity
Volume 2017, Article ID 3719428, 10 pages
https://doi.org/10.1155/2017/3719428
Research Article

Social Network Community Detection Using Agglomerative Spectral Clustering

Department of Computer Engineering, Inha University, Incheon, Republic of Korea

Correspondence should be addressed to Sanggil Kang; rk.ca.ahni@gnakgs

Received 18 April 2017; Revised 24 July 2017; Accepted 23 August 2017; Published 7 November 2017

Academic Editor: Katarzyna Musial

Copyright © 2017 Ulzii-Utas Narantsatsralt and Sanggil Kang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and accuracy of the community detection is still unsatisfactory. Therefore, this paper proposes an agglomerative spectral clustering method with conductance and edge weights. In this method, the most similar nodes are agglomerated based on eigenvector space and edge weights. In addition, the conductance is used to identify densely connected clusters while agglomerating. The proposed method shows improved performance in related works and proves to be efficient for real life complex networks from experiments.