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Critical Node Detection for Maximization of Connected Components: An Extremal Optimization Approach

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16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) (SOCO 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1401))

Abstract

Determining the critical nodes in a network given a certain network measure is a computational challenging problem that requires the design of adaptive and scalable algorithms. The number of connected components in a graph is an example of such a measure: in this case the nodes considered critical are those that, if removed from the network, maximize the number of connected components in the remaining graph. In this paper we approach this problem by using a new algorithm based on Extremal Optimization. Comparisons with existing algorithms conducted on synthetic and real world networks illustrate the potential of the proposed approach.

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2019-1633.

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References

  1. www.individual.utoronto.ca/mventresca/cnd.html

  2. Aringhieri, R., Grosso, A., Hosteins, P., Scatamacchia, R.: A general evolutionary framework for different classes of critical node problems. Eng. Appl. Artif. Intell 55, 128–145 (2016)

    Article  Google Scholar 

  3. Arulselvan, A., Commander, C.W., Pardalos, P.M., Shylo, O.: Managing network risk via critical node identification. In: Risk Management in Telecommunication Networks. Springer, Heidelberg (2007)

    Google Scholar 

  4. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  5. Boettcher, S., Percus, A.G.: Optimization with extremal dynamics. Phys. Rev. Lett. 86, 5211–5214 (2001)

    Article  Google Scholar 

  6. Boettcher, S., Percus, A.G.: Extremal optimization for graph partitioning. Phys. Rev. E 64(2), 026114 (2001)

    Article  Google Scholar 

  7. Boettcher, S., Percus, A.G.: Extremal optimization: an evolutionary local-search algorithm. In: Computational Modeling and Problem Solving in the Networked World, pp. 61–77. Springer, Heidelberg (2003)

    Google Scholar 

  8. Borgatti, S.P.: Identifying sets of key players in a social network. Comput. Math. Organ. Theory 12(1), 21–34 (2006)

    Article  Google Scholar 

  9. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208 (2009)

    Google Scholar 

  10. Chi, K.T., Liu, J., Lau, F.C.: A network perspective of the stock market. J. Empir. Finan 17(4), 659–667 (2010)

    Article  Google Scholar 

  11. De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Extremal optimization applied to load balancing in execution of distributed programs. Appl. Soft Comput. 30, 501–513 (2015)

    Article  Google Scholar 

  12. Emmert-Streib, F., Tripathi, S., Yli-Harja, O., Dehmer, M.: Understanding the world economy in terms of networks: a survey of data-based network science approaches on economic networks. Front. Appl. Math. Stat. 4, 37 (2018)

    Article  Google Scholar 

  13. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  14. Gao, Y.C., Zeng, Y., Cai, S.M.: Influence network in the Chinese stock market. J. Stat. Mech. Theory Exp. 2015(3), P03017 (2015)

    Article  MathSciNet  Google Scholar 

  15. Goh, K.I., Cusick, M.E., Valle, D., Childs, B., Vidal, M., Barabási, A.L.: The human disease network. Proc. Natl. Acad. Sci. 104(21), 8685–8690 (2007)

    Article  Google Scholar 

  16. He, J., Liang, H., Yuan, H.: Controlling infection by blocking nodes and links simultaneously. In: International Workshop on Internet and Network Economics, pp. 206–217. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25510-6_18

  17. Iyer, S., Killingback, T., Sundaram, B., Wang, Z.: Attack robustness and centrality of complex networks. PloS one 8(4), e59613 (2013)

    Article  Google Scholar 

  18. Latora, V., Nicosia, V., Russo, G.: Complex Networks: Principles, Methods and Applications. Cambridge University Press, Cambridge (2017)

    Book  Google Scholar 

  19. Lewis, J.M., Yannakakis, M.: The node-deletion problem for hereditary properties is np-complete. J. Comput. Syst. Sci. 20(2), 219–230 (1980)

    Article  MathSciNet  Google Scholar 

  20. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  21. Lozano, M., García-Martínez, C., Rodriguez, F.J., Trujillo, H.M.: Optimizing network attacks by artificial bee colony. Inf. Sci. 377, 30–50 (2017)

    Article  Google Scholar 

  22. Lung, R.I., Suciu, M., Gaskó, N.: Noisy extremal optimization. Soft Comput. 21(5), 1253–1270 (2015). https://doi.org/10.1007/s00500-015-1858-3

    Article  Google Scholar 

  23. Milo, R., et al.: Superfamilies of evolved and designed networks. Science 303(5663), 1538–1542 (2004)

    Article  Google Scholar 

  24. Opsahl, T.: Why anchorage is not (that) important: binary ties and sample selection (2011)

    Google Scholar 

  25. Reimand, J., Tooming, L., Peterson, H., Adler, P., Vilo, J.: Graphweb: mining heterogeneous biological networks for gene modules with functional significance. Nucleic Acids Res. 36, 452–459 (2008)

    Article  Google Scholar 

  26. Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: AAAI (2015)

    Google Scholar 

  27. Shen, S., Smith, J.C.: Polynomial-time algorithms for solving a class of critical node problems on trees and series-parallel graphs. Networks 60(2), 103–119 (2012)

    Article  MathSciNet  Google Scholar 

  28. Shen, S., Smith, J.C., Goli, R.: Exact interdiction models and algorithms for disconnecting networks via node deletions. Discrete Optimization 9(3), 172–188 (2012)

    Article  MathSciNet  Google Scholar 

  29. Ventresca, M.: Global search algorithms using a combinatorial unranking-based problem representation for the critical node detection problem. Computers & Operations Research 39(11), 2763–2775 (2012)

    Article  MathSciNet  Google Scholar 

  30. Veremyev, A., Prokopyev, O.A., Pasiliao, E.L.: An integer programming framework for critical elements detection in graphs. Journal of Combinatorial Optimization 28(1), 233–273 (2014). https://doi.org/10.1007/s10878-014-9730-4

    Article  MathSciNet  MATH  Google Scholar 

  31. Yang, R., Huang, L., Lai, Y.C.: Selectivity-based spreading dynamics on complex networks. Physical review e 78(2), 026111 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj, Romania

    Noémi Gaskó, Tamás Képes & Mihai Suciu

  2. Centre for the Study of Complexity, Babeş-Bolyai University, Cluj, Romania

    Rodica Ioana Lung

Authors
  1. Noémi Gaskó
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  2. Tamás Képes
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  3. Mihai Suciu
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  4. Rodica Ioana Lung
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Corresponding author

Correspondence to Noémi Gaskó .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. BISITE Research Group, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Gaskó, N., Képes, T., Suciu, M., Lung, R.I. (2022). Critical Node Detection for Maximization of Connected Components: An Extremal Optimization Approach. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_48

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