Research of User-Resource Relationship Based on Intelligent Tags in Evolution Network

  • Shan LiuEmail author
  • Kun Huang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)


In view of the digitization and networking of the current media resources and users, the relationship between users and media resources is studied to realize the maximum effective utilization of media resources and the accurate recommendation of users as well as the more reasonable classification of users and media resources. Based on the attribute tags of users and media resource, we design an evolutionary model that reflects the inner relationship between users and media resource, and study the relevant indicators in the network. This paper mainly uses the modeling and analysis methods of complex networks to design the evolution mechanism and the main structure of the evolution network of user-resource relations. On this basis, we study the inner relationship between the structure and function of the network and provide the foundation for the research and design of various dynamic behaviors and processes in the network. Lay the foundation for the intelligent tag.


Media resources Intelligent tags Complex networks 


  1. 1.
    Liu, Z.: Content-based social tag recommendation technology research. Harbin Engineering University (2012)Google Scholar
  2. 2.
    Wang, N.: Research on complex networks in evolution of virtual industrial clusters. Beijing University of Posts and Telecommunications (2010)Google Scholar
  3. 3.
    Wang, X., Li, X., Chen, G.: Complex network theory and its application. Tsinghua University Press, Beijing (2006)Google Scholar
  4. 4.
    Yin, L., Cheng, F., Ren, Y., et al.: Microblogging topics detection based on complex network overlapping community discovery. J. Sichuan Univ. (Nat. Sci. Ed.), 53(6) (2016)Google Scholar
  5. 5.
    Xian, Z., Tinglei, H., Yi, L.: Network clustering algorithm based on community classification. Comput. Modern. 12, 1–5 (2017)Google Scholar
  6. 6.
    Guo, Y.: Research on complex network community structure detection algorithm. Jilin University (2017)Google Scholar
  7. 7.
    Clauset, A., Moore, C., Newman, M.E.: Hierarchical structure and the prediction of missing links in networks. Nature 453(7191), 98 (2008)CrossRefGoogle Scholar
  8. 8.
    Gupta, A., Thakur, H.K., Garg, A., et al.: Mining and analysis of periodic patterns in weighted directed dynamic network. Int. J. Serv. Sci. Manage. Eng. Technol. 7(1), 1–26 (2016)CrossRefGoogle Scholar
  9. 9.
    Zhang, Q.M., Xu, X.K., Zhu, Y.X., et al.: Measuring multiple evolution mechanisms of complex networks. Sci. Rep. 5, 10350 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Information Engineering SchoolCommunication University of ChinaBeijingChina

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