Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggarwal, C.C.: An introduction to social network data analytics. In: Social Network Data Analytics, pp. 1–15 (2011)
Sun, J., Tang, J.: A survey of models and algorithms for social influence analysis. In: Social Network Data Analytics, pp. 177–214 (2011)
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)
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)
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)
Nerurkar, P., Bhirud, S.: Modeling influence on a social network using interaction characteristics. Int. J. Comput. Math. Sci. 152–160 (2017)
Zurada, J.M.: Introduction to Artificial Neural Systems, vol. 8. West St. Paul (1992)
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)
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)
Breiman, L.: Random forests. Mach. Learn. 5–32 (2001)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)
A gentle introduction to XGBoost for applied machine learning, Sep 2016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nerurkar, P., Pavate, A., Shah, M., Jacob, S. (2019). Analysis of Probabilistic Models for Influence Ranking in Social Networks. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_23
Download citation
DOI: https://doi.org/10.1007/978-981-13-1513-8_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1512-1
Online ISBN: 978-981-13-1513-8
eBook Packages: EngineeringEngineering (R0)