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Mathematical Problems in Engineering
Volume 2016, Article ID 1873504, 11 pages
http://dx.doi.org/10.1155/2016/1873504
Research Article

Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
2State Key Laboratory of NBC Protection for Civilian, Research Institute of Chemical Defense, Beijing 102205, China
3Zhengzhou Coal Industry (Group) Limited Liability Company, Zhengzhou 450000, China

Received 24 September 2015; Revised 16 December 2015; Accepted 20 December 2015

Academic Editor: Juan A. Almendral

Copyright © 2016 Fanrong Meng et al. 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 in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.