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A Local Dynamic Community Detection Algorithm Based on Node Contribution

  • Kun Guo
  • Ling He
  • Jiangsheng Huang
  • Yuzhong ChenEmail author
  • Bing Lin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)

Abstract

The existence of communities in various complex networks is ubiquitous in all aspects of people’s living. Hence, it is crucial to uncover communities accurately, which is one of the hottest research areas in the field of network analysis. Particularly, complex networks are usually in continuous change so that it is more realistic to uncover dynamic communities. In this study, an algorithm based on node contribution for uncovering dynamic communities is proposed. Firstly, the seed nodes are selected via node local fitness in the network, thus guaranteeing that the selected seeds are central nodes of communities. Secondly, a static algorithm is used to obtain communities in initial snapshot of the network. Finally, node contribution is proposed to incrementally uncover communities in non-initial snapshots of the network. The experimental results reveal that our method outperforms all other comparison algorithms in both artificial and real datasets.

Keywords

Complex network Dynamic community detection Node local fitness Node contribution 

Notes

Acknowledgments

This work is partly supported by the National Natural Science Foundation of China under Grant No. 61300104, No. 61300103 and No. 61672159, the Fujian Province High School Science Fund for Distinguished Young Scholars under Grant No. JA12016, the Fujian Natural Science Funds for Distinguished Young Scholar under Grant No. 2015J06014, the Fujian Industry-Academy Cooperation Project under Grant No. 2018H6010 and No. 2017H6008, and Haixi Government Big Data Application Cooperative Innovation Center.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kun Guo
    • 1
    • 2
    • 3
  • Ling He
    • 1
    • 2
    • 3
  • Jiangsheng Huang
    • 4
  • Yuzhong Chen
    • 1
    • 2
    • 3
    Email author
  • Bing Lin
    • 5
  1. 1.College of Mathematics and Computer SciencesFuzhou UniversityFuzhouChina
  2. 2.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information ProcessingFuzhouChina
  3. 3.Key Laboratory of Spatial Data Mining and Information SharingMinistry of EducationFuzhouChina
  4. 4.Power Science and Technology Corporation State Grid Information and Telecommunication GroupFuzhouChina
  5. 5.College of Physics and Energy, Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy MaterialsFujian Normal UniversityFuzhouChina

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