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Frequent Sets Discovery in Privacy Preserving Quantitative Association Rules Mining Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A Discrete Bat Algorithm for the Community Detection Problem

Authors : Eslam A. Hassan, Ahmed Ibrahem Hafez, Aboul Ella Hassanien, Aly A. Fahmy

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

Community detection in networks has raised an important research topic in recent years. The problem of detecting communities can be modeled as an optimization problem where a quality objective function that captures the intuition of a community as a set of nodes with better internal connectivity than external connectivity is selected to be optimized. In this work the Bat algorithmwas used as an optimization algorithm to solve the community detection problem. Bat algorithm is a new Nature-inspired metaheuristic algorithm that proved its good performance in a variety of applications. However, the algorithm performance is influenced directly by the quality function used in the optimization process. Experiments on real life networks show the ability of the Bat algorithm to successfully discover an optimized community structure based on the quality function used and also demonstrate the limitations of the BA when applied to the community detection problem.

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Literature
1.
2.
Ali, A.S, Hussien, A.S, Tolba, M.F, Youssef, A.H.: Visualization of large time-varying vector data. In: 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 4, pp. 210–215. IEEE (2010)
3.
Masdarolomoor, Z., Azmi, R., Aliakbary, S., Riahi, N.: Finding community structure in complex networks using parallel approach. In: 2011 IFIP 9th International Conference on Embedded and Ubiquitous Computing (EUC), pp. 474–479, October 2011
4.
5.
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)CrossRef
6.
Shi, C., Zhong, C., Yan, Z., Cai, Y., Wu, B.: A multi-objective approach for community detection in complex network. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)
7.
Leskovec, J., Lang, K.J, Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web, pp. 631–640. ACM (2010)
8.
Shi, C., Yu, P.S., Cai, Y., Yan, Z., Wu, B.: On selection of objective functions in multi-objective community detection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2301–2304. ACM (2011)
9.
Hafez, A.I., Al-Shammari, E.M., ella Hassanien, A., Fahmy, A.A.: Genetic algorithms for multi-objective community detection in complex networks. In: Pedrycz, W., Chen, S.-M. (eds.) Social Networks: A Framework of Computational Intelligence. Studies in Computational Intelligence, pp. 145–171. Springer, Heidelberg (2014)CrossRef
10.
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010) CrossRef
11.
Yang, X.-S., He, X.: Bat algorithm: literature review and applications. Int. J. Bio-Inspired Comput. 5(3), 141–149 (2013)CrossRef
12.
Shi, C., Wang, Y., Wu, B., Zhong, C.: A new genetic algorithm for community detection. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 5, pp. 1298–1309. Springer, Heidelberg (2009) CrossRef
13.
Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008) CrossRef
14.
Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech Theor. Exp. 9, 9008 (2005)CrossRef
15.
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)
16.
Lusseau, D.: The emergent properties of dolphin social network. Proc. Roy. Soc. Lond. Ser. B Biol. Sci. 270, S186–S188 (2003)CrossRef
17.
McAuley, J.J., Leskovec, J.: Learning to discover social circles in ego networks, pp. 548–556 (2012)
18.
Hafez, A.I., Hassanien, A.E., Fahmy, A.A.: Testing community detection algorithms: a closer look at datasets. In: Panda, M., Dehuri, S., Wang, G.-N. (eds.) Social Networking. ISRL, vol. 65, pp. 87–102. Springer, Heidelberg (2014) CrossRef
19.
Hassan, E.A., Hafez, A.I., Hassanien, A.E., Fahmy, A.A.: Community detection algorithm based on artificial fish swarm optimization. In: Filev, D., et al. (eds.) Intelligent Systems 2014. AISC, pp. 509–521. Springer International Publishing, Heidelberg (2015) CrossRef
20.
Rosvall, M., Axelsson, D., Bergstrom, C.T.: The map equation. Eur. Phy. J. Spec. Top. 178(1), 13–23 (2009)CrossRef
21.
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)CrossRef
22.
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)CrossRef
23.
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), 10008 (2008)CrossRef
24.
Pons, P., Latapy, M.: Computing communities in large networks using random walks (long version 12 (2005). ArXiv Physics e-prints
25.
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)CrossRefMathSciNet
Metadata
Title
A Discrete Bat Algorithm for the Community Detection Problem
Authors
Eslam A. Hassan
Ahmed Ibrahem Hafez
Aboul Ella Hassanien
Aly A. Fahmy
Copyright Year
2015
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-19643-5

Electronic ISBN: 978-3-319-19644-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-19644-2_16

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