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

Fanrong Meng,Jihong Shi,Zhixiao Wang,Yan Xing,Mu Zhu,Feng Zhang

doi:10.1155/2016/1873504

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.

Highlights

In the real world, many systems can be abstracted as a network, such as the Internet, interpersonal relationship networks, disease transmission networks, and scientist cooperation networks
(3) Experimental results in the simulative and real networks show that DBLINK can find overlapping community structure with higher quality compared with some other representative community detection algorithms, while iDBLINK algorithm can be better applied to the dynamic network environment
The abscissa represents the network at each moment, zero coordinates refer to the original network, and ordinate, respectively, shows the community detection quality normalized mutual information (NMI) (a) and the algorithm running time (b)

Summary

Introduction

Many systems can be abstracted as a network, such as the Internet, interpersonal relationship networks, disease transmission networks, and scientist cooperation networks. This paper proposes an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK, which in essence is an extension of DBLINK algorithm. It can update the current affected local community structure according to different network topology changes. (3) Experimental results in the simulative and real networks show that DBLINK can find overlapping community structure with higher quality compared with some other representative community detection algorithms, while iDBLINK algorithm can be better applied to the dynamic network environment

Static Network Community Detection DBLINK

Dynamic Community Detection iDBLINK

Experimental Results

Conclusion