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Evolutionary Centrality and Maximal Cliques in Mobile Social Networks


Affiliations
1 School of Engineering & Information Systems, Morehead State University Morehead, KY, United States
2 Computer Engineering and Computer Science Department, University of Louisville Louisville, KY, United States
 

This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.

Keywords

Centrality, Social Networks, Network Science, Mobile Networks, Evolutionary Centrality, Maximal Cliques.
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  • Xiaohui Zhao, Sai Guo, and Yanlei Wang, “The Node Influence Analysis in Social Networks Based on Structural Holes and Degree Centrality”, IEEE International Conference on Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), pp. 708 – 711, 2017.

  • Felipe Grando, Diego Noble, and Luis C. Lamb, “An Analysis of Centrality Measures for Complex and Social Networks”, 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1-6, 2016.

  • Amedapu Srinivas and R. Leela Velusamy, “Identification of influential nodes from social networks based on Enhanced Degree Centrality Measure”, 2015 IEEE International Advance Computing Conference (IACC), pp. 1179 – 1184, 2015.

  • Zudha Aulia Rachman, Warih Maharani, and Adiwijaya, “The analysis and implementation of degree centrality in weighted graph in Social Network Analysis”, 2013 International Conference of Information and Communication Technology (ICoICT), pp. 72 – 76, 2013.

  • Jie Zhang, Xuerui Ma, Weihao Liu, Yong Bai, “Inferring Community Members in Social Networks by Closeness Centrality Examination”, 2012 Ninth Web Information Systems and Applications Conference, pp. 131-134, 2012.

  • Sergey Brin and Lawrence Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine”, Computer Networks, vol. 30, pp. 107-117, 1998.

  • Eppstein D., Strash D., “Listing All Maximal Cliques in Large Sparse Real-World Graphs”, In: Pardalos P.M., Rebennack S. (eds) Experimental Algorithms. SEA 2011. Lecture Notes in Computer Science, vol 6630. Springer, Berlin, Heidelberg.

  • Cazals, F., Karande, C., “A note on the problem of reporting maximal cliques”, Theoretical Computer Science, 407(1-3), pp. 564–568, 2008.

  • F. Cazals, C. Karande, “An algorithm for reporting maximal c-cliques”, Theoretical Computer Science, 349 (3), pp. 484-490, 2005.

  • David Eppstein, Maarten Löffler, Darren Strash, “Listing All Maximal Cliques in Large Sparse Real World Graphs”, Journal of Experimental Algorithmics (JEA), Vol. 18, 2013.

  • Anne-Sophie Himmel, Hendrik Molter, Rolf Niedermeier, Manuel Sorge, “Adapting the BronKerbosch Algorithm for Enumerating Maximal Cliques in Temporal Graphs”, Social Network Analysis and Mining, pp. 7-35, 2017.

  • Katy Börner, Soma Sanyal, and Alessandro Vespignani, “Network Science”, Annual Review of Information Science & Technology, Volume 41, pp. 537-607, January 2007.

  • Nathan Eagle, Alex Pentland, and David Lazer. Inferring Social Network Structure using Mobile Phone Data, Proceedings of the National Academy of Sciences (PNAS), Vol 106 (36), pp. 15274-15278, 2009.


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  • Evolutionary Centrality and Maximal Cliques in Mobile Social Networks

Abstract Views: 379  |  PDF Views: 138

Authors

Heba Elgazzar
School of Engineering & Information Systems, Morehead State University Morehead, KY, United States
Adel Elmaghraby
Computer Engineering and Computer Science Department, University of Louisville Louisville, KY, United States

Abstract


This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.

Keywords


Centrality, Social Networks, Network Science, Mobile Networks, Evolutionary Centrality, Maximal Cliques.

References