Advertisement

Analysis of Complex Data by Means of Complex Networks

  • Massimiliano Zanin
  • Ernestina Menasalvas
  • Stefano Boccaletti
  • Pedro A. Sousa
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 423)

Abstract

In the ever-increasing availability of massive data sets describing complex systems, i.e. systems composed of a plethora of elements interacting in a non-linear way, complex networks have emerged as powerful tools for characterizing these structures of interactions in a mathematical way. In this contribution, we explore how different Data Mining techniques can be adapted to improve such characterization. Specifically, we here describe novel techniques for optimizing network representations of different data sets; automatize the extraction of relevant topological metrics, and using such metrics toward the synthesis of high-level knowledge. The validity and usefulness of such approach is demonstrated through the analysis of medical data sets describing groups of control subjects and patients. Finally, the application of these techniques to other social and technological problems is discussed.

Keywords

Complex systems complex networks data mining 

References

  1. 1.
    Knoke, D., Yang, S.: Social Network Analysis. Sage (2008)Google Scholar
  2. 2.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10, 186–198 (2009)CrossRefGoogle Scholar
  3. 3.
    Zanin, M., Lillo, F.: Modelling the air transport with complex networks: A short review. The European Physical Journal Special Topics 215, 5–21 (2013)CrossRefGoogle Scholar
  4. 4.
    Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406, 378–382 (2000)CrossRefGoogle Scholar
  5. 5.
    Strano, E., Zanin, M., Estrada, E., Lillo, F.: Spatially embedded socio-technical complex networks. The European Physical Journal Special Topics 215, 1–4 (2013)CrossRefGoogle Scholar
  6. 6.
    Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74 (2002)Google Scholar
  7. 7.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics Reports 424, 175–308 (2006)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Costa, L.D.F., Rodrigues, F.A., Travieso, G., Villas Boas, P.R.: Characterization of complex networks: A survey of measurements. Advances in Physics 56, 167–242 (2007)CrossRefGoogle Scholar
  9. 9.
    Costa, L.D.F., Oliveira Jr, O.N., Travieso, G., Rodrigues, F.A., Villas Boas, P.R., Antiqueira, L., Viana, M.P., Correa Rocha, L.E.: Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Advances in Physics 60, 329–412 (2011)CrossRefGoogle Scholar
  10. 10.
    Zanin, M.: Complex Networks and Data Mining: Toward a new perspective for the understanding of Complex Systems. PhD Thesis (2014)Google Scholar
  11. 11.
    Giannotti, F., Pedreschi, D., Pentland, A., Lukowicz, P., Kossmann, D., Crowley, J., Helbing, D.: A planetary nervous system for social mining and collective awareness. The European Physical Journal Special Topics 214, 49–75 (2012)CrossRefGoogle Scholar
  12. 12.
    Havlin, S., Kenett, D.Y., Ben-Jacob, E., Bunde, A., Cohen, R., Hermann, H., Kantelhardt, J.W., Kertész, J., Kirkpatrick, S., Kurths, J., Portugali, J., Solomon, S.: Challenges in network science: Applications to infrastructures, climate, social systems and economics. The European Physical Journal Special Topics 214, 273–293 (2012)CrossRefGoogle Scholar
  13. 13.
    Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Reviews of Modern Physics 81 (2009)Google Scholar
  14. 14.
    Pastor-Satorras, R., Vespignani, A.: Epidemic dynamics and endemic states in complex networks. Physical Review E 63, 066117 (2001)Google Scholar
  15. 15.
    Navigli, R., Velardi, P., Faralli, S.: A graph-based algorithm for inducing lexical taxonomies from scratch. In: Twenty-Second International Joint Conference on Artificial Intelligence-Volume, pp. 1872–1877. AAAI Press (2011)Google Scholar
  16. 16.
    Mantegna, R.N.: Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems 11, 193–197 (1999)CrossRefGoogle Scholar
  17. 17.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002)CrossRefGoogle Scholar
  18. 18.
    Phelps, M.E., Mazziotta, J.C.: Positron emission tomography: human brain function and biochemistry. Science 228, 799–809 (1985)CrossRefGoogle Scholar
  19. 19.
    Zanin, M., Boccaletti, S.: Complex networks analysis of obstructive nephropathy data. Chaos: An Interdisciplinary Journal of Nonlinear Science 21, 033103 (2011)Google Scholar
  20. 20.
    Zanin, M., Alcazar, J.M., Carbajosa, J.V., Sousa, P., Papo, D., Menasalvas, E., Boccaletti, S.: Parenclitic networks’ representation of data sets. arXiv:1304.1896 (2013)Google Scholar
  21. 21.
    Zanin, M., Sousa, P., Papo, D., Bajo, R., García-Prieto, J., del Pozo, F., Menasalvas, E., Boccaletti, S.: Optimizing functional network representation of multivariate time series. Scientific Reports 2 (2012)Google Scholar
  22. 22.
    Steinwart, I., Christmann, A.: Support vector machines. Springer (2008)Google Scholar
  23. 23.
    Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning. Springer (2006)Google Scholar
  24. 24.
    Buldú, J.M., Bajo, R., Maestú, F., Castellanos, N., Leyva, I., Gil, P., Sendiña-Nadal, I., Almendral, J.A., Nevado, A.: del-Pozo, F., Boccaletti, S.: Reorganization of functional networks in mild cognitive impairment. PLoS One 6, e19584 (2011)Google Scholar
  25. 25.
    Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer Networks. arXiv:1309.7233 [physics.soc-ph] (2013)Google Scholar
  26. 26.
    Cardillo, A., Gómez-Gardeñes, J., Zanin, M., Romance, M., Papo, D., del Pozo, F., Boccaletti, S.: Emergence of network features from multiplexity. Scientific Reports 3 (2013)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Massimiliano Zanin
    • 1
    • 2
    • 3
  • Ernestina Menasalvas
    • 2
  • Stefano Boccaletti
    • 4
  • Pedro A. Sousa
    • 1
  1. 1.Faculdade de Ciências e Tecnologia, Departamento de Engenharia ElectrotécnicaUniversidade Nova de LisboaCaparicaPortugal
  2. 2.Center for Biomedical TechnologyUniversidad Politécnica de MadridMadridSpain
  3. 3.Innaxis Foundation & Research InstituteMadridSpain
  4. 4.CNR - Institute of Complex SystemsSesto FiorentinoItaly

Personalised recommendations