Analysis of Probabilistic Models for Influence Ranking in Social Networks

  • Pranav NerurkarEmail author
  • Aruna Pavate
  • Mansi Shah
  • Samuel Jacob
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)


Influence is a phenomenon occurring in every social network. Network science literature on Influence ranking focuses on investigation and design of computational models for ranking of nodes by their influence and mapping the spread of their influence in the network. In addition to this contemporary literature seeks efficient and scalable influence ranking techniques that could be suitable for application on massive social networks. For this purpose joint and conditional probabilistic models could be a way forward as these models can be trained on data rapidly making them ideal for deployment on massive social networks. However identification of suitable predictors that may have a correlation with influence plays a major role in deciding the successful outcome for these models. The present investigation proceeds with the intuition that interaction is positively correlated with influence. Furthermore, through extensive experimentation it identifies a joint probabilistic model and trains it on interaction characteristics on nodes of a social network for influence ranking. A qualitative analysis of these models is presented to highlight its suitability.


Social network analysis Social influence analysis Network centrality Influence Ranking 


  1. 1.
    Aggarwal, C.C.: An introduction to social network data analytics. In: Social Network Data Analytics, pp. 1–15 (2011)CrossRefGoogle Scholar
  2. 2.
    Sun, J., Tang, J.: A survey of models and algorithms for social influence analysis. In: Social Network Data Analytics, pp. 177–214 (2011)CrossRefGoogle Scholar
  3. 3.
    Scripps, J., Tan, P.-N., Esfahanian, A.-H.: Measuring the effects of preprocessing decisions and network forces in dynamic network analysis. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 747–756. ACM (2009)Google Scholar
  4. 4.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)Google Scholar
  5. 5.
    Rao, A., Spasojevic, N., Li, Z., DSouza, T.: Klout score: measuring influence across multiple social networks. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2282–2289. IEEE (2015)Google Scholar
  6. 6.
    Nerurkar, P., Bhirud, S.: Modeling influence on a social network using interaction characteristics. Int. J. Comput. Math. Sci. 152–160 (2017)Google Scholar
  7. 7.
    Zurada, J.M.: Introduction to Artificial Neural Systems, vol. 8. West St. Paul (1992)Google Scholar
  8. 8.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks, 2004. Proceedings, vol. 2, pp. 985–990. IEEE (2004)Google Scholar
  9. 9.
    Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)Google Scholar
  10. 10.
    Breiman, L.: Random forests. Mach. Learn. 5–32 (2001)Google Scholar
  11. 11.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)Google Scholar
  12. 12.
    A gentle introduction to XGBoost for applied machine learning, Sep 2016Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Pranav Nerurkar
    • 1
    Email author
  • Aruna Pavate
    • 2
  • Mansi Shah
    • 3
  • Samuel Jacob
    • 4
  1. 1.Department of CE & ITVJTIMumbaiIndia
  2. 2.Department of CE & ITAtharva CoEMumbaiIndia
  3. 3.Department of CE & ITRizvi CoEMumbaiIndia
  4. 4.Jagdishprasad Jhabarmal Tibrewala UniversityJhunjhunuIndia

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