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

Author

Listed:
  • Fanrong Meng
  • Feng Zhang
  • Mu Zhu
  • Yan Xing
  • Zhixiao Wang
  • Jihong Shi

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.

Suggested Citation

  • Fanrong Meng & Feng Zhang & Mu Zhu & Yan Xing & Zhixiao Wang & Jihong Shi, 2016. "Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-11, January.
    • Handle: RePEc:hin:jnlmpe:1873504
    DOI: 10.1155/2016/1873504
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