Global User Social Networking Ranking Statistics

  • P. AjithaEmail author
  • Bana Suresh Kumar Reddy
  • Balina Prudhvi
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)


Long range social correspondence affiliations are open at a couple on-line systems like and, wherever a couple of customers keep coordinating with one another dependably. One captivating and principal trouble inside the long range social correspondence affiliations is to rank customers kept up their centrality promisingly. Frill in nursing right masterminding once-over of customer centrality may benefit a couple of get-togethers in social connection affiliations like the types of progress suppliers and site heads. Regardless of the way in which that it’s incredibly promising to get a significance based organizing once-over of customers, there square measure a couple of express troubles because of the enormous scale and fragments of individual to particular correspondence information. In the midst of this paper, we will by and large propose a lone perspective to achieve this goal is studying customer hugeness by separating the dynamic encouraged undertakings among customers on accommodating affiliations.


Passed on structures Watching data Social affiliations Customer development Significance 


  1. 1.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65–74. ACM (2011)Google Scholar
  2. 2.
    Brin, S., Page, L.: Reprint of: the anatomy of a large scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)CrossRefGoogle Scholar
  3. 3.
    Brown, R.G.: Smoothing, Forecasting and Prediction of Discrete Time Series. Courier Corporation (2004)Google Scholar
  4. 4.
    Campbell, C.S., Maglio, P.P., Cozzi, A., Dom, B.: Expertise identification using email communications. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 528–531. ACM (2003)Google Scholar
  5. 5.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: the million follower fallacy. In: ICWSM 2010, pp. 10–17 (2010)Google Scholar
  6. 6.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM (2001)Google Scholar
  7. 7.
    Brown, P.E., Feng, J.: Measuring user influence on twitter using modified k-shell decomposition (2011)Google Scholar
  8. 8.
    Jiao, J., Yan, J., Zhao, H., Fan, W.: Expertrank: an expert user ranking algorithm in online communities. In: International Conference on New Trends in Information and Service Science, NISS 2009, pp. 674–679. IEEE (2009)Google Scholar
  9. 9.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM (JACM) 46(5), 604–632 (1999)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kumar, S., Morstatter, F., Liu, H.: Twitter Data Analytics. Springer (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • P. Ajitha
    • 1
    Email author
  • Bana Suresh Kumar Reddy
    • 2
  • Balina Prudhvi
    • 2
  1. 1.Department of Information and TechnologySathyabama Institute of Science and TechnologyChennaiIndia
  2. 2.Information TechnologySathyabama Institute of Science and TechnologyChennaiIndia

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