Abstract
Identifying important nodes in a network is an important area of research in network science. m-ranking method is a method proposed Reji Kumar et al. [18] for ranking the nodes in a network which avoids the chance of assigning same rank for two nodes with different physical characteristics. This ranking takes into account the degree of all nodes and weights of all edges in a network. As the network becomes bigger and bigger the m-ranking method takes more and more time to complete. To overcome this difficulty in this paper we propose an approximation to this method, which simplifies the calculations without undermining the ranking outcome. We illustrate the procedure in some example networks.
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References
Zeng, A., Zhang, C.-J.: Ranking spreaders by decomposing complex networks. Phys. Lett. A 377, 1031–1035 (2013)
Brin, S., Page, L.: The anatomy of a large- scale hyper textual web search engine. Comput. New. ISDN Syst. 30, 107–117 (1998)
Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, Cambridge (2010)
Garas, A., Shweitzer, F., Havlin, S.: A k-shell decomposition method for weighted networks. New J. Phys. 4, 083030 (2012)
Jaber, L.B., Tamine, L.: Active micro bloggers: identifying influencers, leaders in micro blogging networks. Springer (2012)
Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton (2010)
Bae, J., Kim, S.: Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Phys. A 395, 549–559 (2014)
Li, Q., Zhou, T., Lu, L.: Identifying influential spreaders by weighted leader rank. Phys. A 404, 47–55 (2014)
Bordons, M.: The relationship between the research performance of scientists and their position in co - authorship networks. J. Inform. 9, 135–144 (2015)
Rosnow, R.L., Foster, E.K.: We should distinguish between rumors and gossip as each appears to function differently in its pure state. Psychological Science Agenda, American Psychological Science Association, April 2005
Reji Kumar, K., Manuel, S.: Spreading information in complex networks: a modified method. In: Proceedings of the International Conference on Emerging Technological Trends. IEEE Digital Explore Library (2016)
Reji Kumar, K., Manuel, S.: Personal influence on the spreading of ınformation – a network based study. Int. J. Math. Trends Technol. 52(8), 570–573 (2017)
Reji Kumar, K., Manuel, S., Satheesh, E.N.: Spreading information in complex networks: an overview and some modified methods. In: Graph Theory Advanced Algorithms and Applications. Intechopen (2017)
Reji Kumar, K., Manuel, S.: A centrality measure for directed networks: m-ranking method. Zyer, T., et al. (eds.) Social Networks and Surveillance for Society. Lecture Notes in Social Networks. Springer, Heidelberg (2019)
Reji Kumar, K., Manuel, S.: Collaborations of Indian institutions which conduct mathematical research: a study from the perspective of social network analysis. Scientometrics. https://doi.org/10.1007/s11192-018-2898-0
Reji Kumar, K., Manuel, S.: A network analysis of the contributions of Kerala in the field of mathematical research over the last three decades. In: Proceedings of the International Conference on Computer Networks, Big Data and IoT (ICCBI 2018) (2018)
Manuel, S., Reji Kumar, K.: An improved k-shell decomposition for complex networks based on potential edge weights. Int. J Appl. Math. Sci. 9(2), 163–168 (2016)
Manuel, S., Reji Kumar, K., Benson, D.: The m-Ranking of nodes in complex networks. In: Proceedings of 9th International COMSNETS 2017 (2017)
Lewis, T.G.: Network Science: Theory and Practice. Wiley, Hoboken (2009)
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)
Acknowledgements
First author would like to acknowledge the research facilities extended by Institute of Mathematical Sciences, Chennai by granting the Associate Visitor-ship. A part of the research is completed during this period. He also likes to acknowledge the financial support of UGC in the form insert of a major research project No. 40-243/2011(SR). A part of this research is completed during this visit. The second author would like to thank the financial support given to him by UGC in the form of FDP (FIP/12th Plan/KLMG018 TF06).
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Reji Kumar, K., Manuel, S. (2020). An Approximation to m-Ranking Method in Networks. In: Balaji, S., Rocha, Á., Chung, YN. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_33
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DOI: https://doi.org/10.1007/978-3-030-28364-3_33
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