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The Prediction Model of Online Social Networks’ Evolution Based on the Similarity of Community

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 13))

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Abstract

It is one of the key problems in network research to discover the evolution of community in social network, study the evolution of community and forecast the development trend of community. How to more faithfully reveal the evolution of the community in complex networks has always been the goal pursued by all researchers. Based on the evolutionary prediction model of event-based community, this paper constructs four kinds of event framework models by using community similarity algorithm and edge similarity algorithm, and through the WS small world network simulation data set test. In the premise of improving the accuracy of prediction, it does not significantly reduce the number of community events found, with good predictive performance.

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Author information

Authors and Affiliations

  1. Engineering University of CAPF, Xi’an, People’s Republic of China

    Li Xiaolong & Zhang Deyang

Authors
  1. Li Xiaolong
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  2. Zhang Deyang
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Corresponding author

Correspondence to Li Xiaolong .

Editor information

Editors and Affiliations

  1. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  2. Open University of Catalonia, Barcelona, Spain

    Santi Caballé

  3. Fukuoka Institute of Technology (FIT), Fukuoka, Japan

    Leonard Barolli

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Xiaolong, L., Deyang, Z. (2018). The Prediction Model of Online Social Networks’ Evolution Based on the Similarity of Community. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-69835-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69834-2

  • Online ISBN: 978-3-319-69835-9

  • eBook Packages: EngineeringEngineering (R0)

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