A Dynamic Modularity Based Community Detection Algorithm for Large-scale Networks: DSLM

Aktunc R., TOROSLU İ. H., Ozer M., Davulcu H.

IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Paris, France, 25 - 28 August 2015, pp.1177-1183 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1145/2808797.2808822
  • City: Paris
  • Country: France
  • Page Numbers: pp.1177-1183
  • Middle East Technical University Affiliated: Yes


In this work, a new fast dynamic community detection algorithm for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by modifying well-known static modularity based community detection algorithm. The proposed framework is tested using several different datasets. According to our results, community detection algorithms in the proposed framework perform better than static algorithms when large scale dynamic networks are considered.