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Dynamic Community Detection Algorithm Based on Automatic Parameter Adjustment

  • Kai Lu
  • Xin WangEmail author
  • Xiaoping Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10585)

Abstract

Community detection is widely used in social network analysis. It clusters densely connected vertices into communities. As social networks get larger, scalable algorithms are drawing more attention. Among those methods, the algorithm named Attractor is quite outstanding both in terms of accuracy and scalability. However, it is highly dependent on the parameter, which is abstract for users. The improper parameter value can bring about some problems. There can be a huge community (monster) sometimes; other time the communities are generally too small (fragments). The existing fragments also need eliminating. Such phenomenon greatly deteriorates the performance of Attractor. We modify the algorithm and propose mAttractor, which adjusts the parameter automatically. We introduce two constraints to limit monsters and fragments and to narrow the parameter range. An optional parameter is also introduced. The proposed algorithm can choose to satisfy or ignore the optional parameter by judging whether it is reasonable. Our algorithm also eliminates the existing fragments. A delicate pruning is designed for fast determination. Experiments show that our mAttractor outperforms Attractor by 2%–270%.

Keywords

Community detection Social network Data mining 

Notes

Acknowledgement

This work is partially supported by The National Key Research and Development Program of China (2016YFB0200401), by program for New Century Excellent Talents in University, by National Science Foundation (NSF) China 61402492, 61402486, 61379146, by the laboratory pre-research fund (9140C810106150C81001).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of Computer ScienceNational University of Defense TechnologyChangshaPeople’s Republic of China
  2. 2.Science and Technology on Parallel and Distributed Processing LaboratoryNational University of Defense TechnologyChangshaPeople’s Republic of China

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