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Community Detection in Weighted Directed Networks Using Nature-Inspired Heuristics

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Book cover Intelligent Data Engineering and Automated Learning – IDEAL 2018 (IDEAL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11315))

Abstract

Finding groups from a set of interconnected nodes is a recurrent paradigm in a variety of practical problems that can be modeled as a graph, as those emerging from Social Networks. However, finding an optimal partition of a graph is a computationally complex task, calling for the development of approximative heuristics. In this regard, the work presented in this paper tackles the optimal partitioning of graph instances whose connections among nodes are directed and weighted, a scenario significantly less addressed in the literature than their unweighted, undirected counterparts. To efficiently solve this problem, we design several heuristic solvers inspired by different processes and phenomena observed in Nature (namely, Water Cycle Algorithm, Firefly Algorithm, an Evolutionary Simulated Annealing and a Population based Variable Neighborhood Search), all resorting to a reformulated expression for the well-known modularity function to account for the direction and weight of edges within the graph. Extensive simulations are run over a set of synthetically generated graph instances, aimed at elucidating the comparative performance of the aforementioned solvers under different graph sizes and levels of intra- and inter-connectivity among node groups. We statistically verify that the approach relying on the Water Cycle Algorithm outperforms the rest of heuristic methods in terms of Normalized Mutual Information with respect to the true partition of the graph.

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References

  1. Aldecoa, R., Marín, I.: Deciphering network community structure by surprise. PloS ONE 6(9), e24195 (2011)

    Article  Google Scholar 

  2. Bello-Orgaz, G., Jung, J.J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)

    Article  Google Scholar 

  3. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  4. Chakraborty, T., Dalmia, A., Mukherjee, A., Ganguly, N.: Metrics for community analysis: a survey. ACM Comput. Surv. (CSUR) 50(4), 54 (2017)

    Article  Google Scholar 

  5. Chakraborty, T., Srinivasan, S., Ganguly, N., Mukherjee, A., Bhowmick, S.: On the permanence of vertices in network communities. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1396–1405. ACM (2014)

    Google Scholar 

  6. Cockbain, E., Brayley, H., Laycock, G.: Exploring internal child sex trafficking networks using social network analysis. Policing: J. Policy Pract. 5(2), 144–157 (2011)

    Article  Google Scholar 

  7. Del Ser, J., Lobo, J.L., Villar-Rodriguez, E., Bilbao, M.N., Perfecto, C.: Community detection in graphs based on surprise maximization using firefly heuristics. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2233–2239. IEEE (2016)

    Google Scholar 

  8. Eskandar, H., Sadollah, A., Bahreininejad, A., Hamdi, M.: Water cycle algorithm - a novel metaheuristic optimization method for solving constrained engineering optimization problems. Appl. Soft Comput. 110(111), 151–166 (2012)

    Google Scholar 

  9. Falkenauer, E.: Genetic Algorithms and Grouping Problems. Wiley, New York (1998)

    MATH  Google Scholar 

  10. Guerrero, M., Montoya, F.G., Baños, R., Alcayde, A., Gil, C.: Adaptive community detection in complex networks using genetic algorithms. Neurocomputing 266, 101–113 (2017)

    Article  Google Scholar 

  11. Hafez, A.I., Zawbaa, H.M., Hassanien, A.E., Fahmy, A.A.: Networks community detection using artificial bee colony swarm optimization. In: Kömer, P., Abraham, A., Snášel, V. (eds.) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. AISC, vol. 303, pp. 229–239. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08156-4_23

    Chapter  Google Scholar 

  12. Harris, J.M., Hirst, J.L., Mossinghoff, M.J.: Combinatorics and Graph Theory, vol. 2. Springer, New York (2008). https://doi.org/10.1007/978-0-387-79711-3

    Book  MATH  Google Scholar 

  13. Hassan, E.A., Hafez, A.I., Hassanien, A.E., Fahmy, A.A.: A discrete bat algorithm for the community detection problem. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 188–199. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19644-2_16

    Chapter  Google Scholar 

  14. Honghao, C., Zuren, F., Zhigang, R.: Community detection using ant colony optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 3072–3078. IEEE (2013)

    Google Scholar 

  15. Hruschka, E.R., Campello, R.J., Freitas, A.A.: A survey of evolutionary algorithms for clustering. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 39(2), 133–155 (2009)

    Article  Google Scholar 

  16. Jia, G., et al.: Community detection in social and biological networks using differential evolution. In: Hamadi, Y., Schoenauer, M. (eds.) LION 2012. LNCS, pp. 71–85. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34413-8_6

    Chapter  Google Scholar 

  17. Lara-Cabrera, R., Pardo, A.G., Benouaret, K., Faci, N., Benslimane, D., Camacho, D.: Measuring the radicalisation risk in social networks. IEEE Access 5, 10892–10900 (2017)

    Article  Google Scholar 

  18. Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118703 (2008)

    Article  Google Scholar 

  19. Lu, H., Halappanavar, M., Kalyanaraman, A.: Parallel heuristics for scalable community detection. Parallel Comput. 47, 19–37 (2015)

    Article  MathSciNet  Google Scholar 

  20. Newman, M.E.: Analysis of weighted networks. Phys. Rev. E 70(5), 056131 (2004)

    Article  Google Scholar 

  21. Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Article  Google Scholar 

  22. Osaba, E., Del Ser, J., Sadollah, A., Bilbao, M.N., Camacho, D.: A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem. Appl. Soft Comput. 71, 277–290 (2018)

    Article  Google Scholar 

  23. Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87700-4_107

    Chapter  Google Scholar 

  24. Pizzuti, C.: Evolutionary computation for community detection in networks: a review. IEEE Trans. Evol. Comput. 22(3), 464–483 (2018)

    Article  Google Scholar 

  25. Rahimi, S., Abdollahpouri, A., Moradi, P.: A multi-objective particle swarm optimization algorithm for community detection in complex networks. Swarm Evol. Comput. 39, 297–309 (2018)

    Article  Google Scholar 

  26. Shi, C., Liu, Y., Zhang, P.: Weighted community detection and data clustering using message passing. J. Stat. Mech.: Theory Exp. 2018(3), 033405 (2018)

    Article  MathSciNet  Google Scholar 

  27. Tasgin, M., Herdagdelen, A., Bingol, H.: Community detection in complex networks using genetic algorithms. arXiv preprint arXiv:0711.0491 (2007)

  28. Villar-Rodríguez, E., Del Ser, J., Torre-Bastida, A.I., Bilbao, M.N., Salcedo-Sanz, S.: A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines. Concurr. Comput.: Pract. Exp. 28(4), 1385–1395 (2016)

    Article  Google Scholar 

  29. Wang, X., Tang, L.: A population-based variable neighborhood search for the single machine total weighted tardiness problem. Comput. Oper. Res. 36(6), 2105–2110 (2009)

    Article  Google Scholar 

  30. Westlake, B.G., Bouchard, M.: Liking and hyperlinking: community detection in online child sexual exploitation networks. Soc. Sci. Res. 59, 23–36 (2016)

    Article  Google Scholar 

  31. Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspir. Comput. 2(2), 78–84 (2010)

    Article  Google Scholar 

  32. Yip, P.P., Pao, Y.H.: Combinatorial optimization with use of guided evolutionary simulated annealing. IEEE Trans. Neural Netw. 6(2), 290–295 (1995)

    Article  Google Scholar 

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Acknowledgements

E. Osaba and J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program. I. Fister Jr. and I. Fister acknowledge the financial support from the Slovenian Research Agency (Research Core Fundings No. P2-0041 and P2-0057). A. Iglesias and A. Galvez acknowledge the financial support from the projects TIN2017-89275-R (AEI/FEDER, UE), PDE-GIR (H2020, MSCA program, ref. 778035), and JU12 (SODERCAN/FEDER UE).

Author information

Authors and Affiliations

  1. TECNALIA, 48160, Derio, Spain

    Eneko Osaba & Javier Del Ser

  2. University of the Basque Country (UPV/EHU), 48013, Bilbao, Spain

    Javier Del Ser

  3. Basque Center for Applied Mathematics (BCAM), 48009, Bilbao, Spain

    Javier Del Ser

  4. Universidad Autonoma de Madrid, 28049, Madrid, Spain

    David Camacho

  5. Universidad de Cantabria, 39005, Santander, Spain

    Akemi Galvez & Andres Iglesias

  6. Toho University, Funabashi, Japan

    Akemi Galvez & Andres Iglesias

  7. University of Maribor, Maribor, Slovenia

    Iztok Fister Jr. & Iztok Fister

Authors
  1. Eneko Osaba
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  2. Javier Del Ser
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  3. David Camacho
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  4. Akemi Galvez
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  5. Andres Iglesias
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  6. Iztok Fister Jr.
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  7. Iztok Fister
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Corresponding author

Correspondence to Eneko Osaba .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Rm 209, Building B, Autonomous University of Madrid, Madrid, Spain

    David Camacho

  3. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

  4. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Osaba, E. et al. (2018). Community Detection in Weighted Directed Networks Using Nature-Inspired Heuristics. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_36

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  • DOI: https://doi.org/10.1007/978-3-030-03496-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03495-5

  • Online ISBN: 978-3-030-03496-2

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