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A node-priority based large-scale overlapping community detection using evolutionary multi-objective optimization

  • Zhengyi Chai
  • Shijiao LiangEmail author
Special Issue
  • 24 Downloads

Abstract

Community structure is one of the most important features in complex networks. However, with increasing of network scale, some existing methods cannot effectively detect the community structure of complex network, and the available methods mostly aimed at non-overlapping networks. In this paper, we focus on overlapping community detection in large-scale networks, because most of the communities in real-world networks are overlapped. In order to improve the accuracy of large-scale overlapping community detection, we suggest a community detection method based on node priority. The proposed algorithm has two advantages: (1) We define a priority function \({\text{f}}_{\text{NN}}\) to assess the closeness between adjacent nodes. It explores the potential community structure in advance and reduces the scale of networks. (2) We employ NSGA-II and select all Pareto fronts to mine large-scale overlapping communities. The proposed algorithm is tested by the artificial and real datasets. The results show that the proposed algorithm can effectively improve the accuracy of community detection and has better optimization effect.

Keywords

Community detection Multi-objective optimization Large-scale network Pareto fronts Node priority 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. U1504613).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin Polytechnic UniversityTianjinChina

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