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Local community detection for multi-layer mobile network based on the trust relation

  • XiaoMing Li
  • Qiang Tian
  • Minghu Tang
  • Xue Chen
  • Xiaoxian YangEmail author
Article
  • 27 Downloads

Abstract

With the fast development of mobile Internet, people’s social exchange media has transformed from the traditional social network to mobile network. With the explosion of massive information, it has become an interesting topic to detect network user groups with close correlation in the mobile social network. These groups are hidden in the continuously changing relations of social network, and it is very difficult to obtain the information of entire social network. In addition, these social relations are intertwined and complicated under the influence of various networks, and as a result, researches on single-layer network are simple and incomplete. Therefore, this paper proposed a local community detection algorithm for multi-layer complicated network based on the trust relation (MTLCD) to constrain the node tensor. We compared the performance of our algorithm with other classic network clustering algorithms such as GL, LART and PMM in four actual multi-layer network datasets of Bio GRID, Remote sensing, Twitter and Mobile QQ Zone, and the multi-layer modularity was used as the measurement index to evaluate the algorithm performance. The experimental results and analysis prove that: in the MTLCD algorithm, the core node obtained based on the trust relation can better identify the local community in dataset with trust relation. In addition, we also found that this algorithm had higher accuracy and stability, and it can accurately reflect the local community structure which the core node belongs to.

Keywords

Mobile social multi-layer network Local community detection Trust relation Tensor 

Notes

Funding

This work was supported by the Fundamental Research of Xinjiang Corps 2016AC015, and the Applied Basic Research Project of Qinghai Province No: 2018-ZJ-707, and the Youth Foundation of Shanghai Polytechnic University under Grant No. EGD18XQD01; the CERNET Innovation Project No. NGII2017 0513.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Intelligence and ComputingTianjin UniversityTianjinPeople’s Republic of China
  2. 2.School of Information Science and EngineeringZaozhuang UniversityZaozhuangPeople’s Republic of China
  3. 3.Tianjin Normal UniversityTianjinPeople’s Republic of China
  4. 4.Qinghai Nationalities UniversityQinghaiPeople’s Republic of China
  5. 5.School of Computer and Information EngineeringShanghai Polytechnic UniversityShanghaiPeople’s Republic of China

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