Optimizing functional network representation of multivariate time series

Scientific Reports

Volumn 2, Issue , 2012, Pages

  Zanin, Massimiliano ^a,b,c   Sousa, Pedro ^a   Papo, David ^b   Bajo, Ricardo ^b   García Prieto, Juan ^b   Pozo, Francisco Del ^b   Menasalvas, Ernestina ^b   Boccaletti, Stefano ^b  

^a UNIVERSIDADE NOVA DE LISBOA   (Portugal)

^b UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^c Innaxis Foundation and Research Institute   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; CASE CONTROL STUDY; HUMAN; MAGNETOENCEPHALOGRAPHY; MILD COGNITIVE IMPAIRMENT; MULTIVARIATE ANALYSIS; NONLINEAR SYSTEM; PATHOPHYSIOLOGY; PSYCHOLOGICAL ASPECT; SHORT TERM MEMORY; SUPPORT VECTOR MACHINE;

CASE-CONTROL STUDIES; HUMANS; MAGNETOENCEPHALOGRAPHY; MEMORY, SHORT-TERM; MILD COGNITIVE IMPAIRMENT; MODELS, BIOLOGICAL; MULTIVARIATE ANALYSIS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; SUPPORT VECTOR MACHINES;

EID: 84866058332     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep00630     Document Type: Article

References (27)

1. Boccaletti, S., Latora, V.,Moreno, Y., Chavez, M.&Hwang,D. Complex networks: Structure and dynamics. Phys. Rep. 424, 175-308 (2006). (Pubitemid 43135890)
2. Amaral, L. A. N. & Ottino, J. M. Complex networks-Augmenting the framework for the study of complex systems. Eur. Phys. J. B 38, 147-162 (2004).
3. Castellano, C., Fortunato, S. & Loreto, V. Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591 (2009).
4. Bullmore, E. T. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198 (2009).
5. Egúluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M. & Apkarian, A. V. Scalefree brain functional networks. Phys. Rev. Lett. 94, 018102 (2005).
6. Watts, D. J. A simple model of global cascades on random networks. Proc. Natl. Acad. Sci. USA 99, 5766 (2002).
7. Llas, M., Gleiser, P. M., López, J. M. & Díaz-Guilera, A. Nonequilibrium phase transition in amodel for the propagation of innovations among economic agents. Phys. Rev. E 68, 066101 (2003).
8. Guardiola, X., Díaz-Guilera, A., Pérez, C. J., Arenas, A. & Llas, M. Modeling diffusion of innovations in a social network. Phys. Rev. E 66, 026121 (2002).
9. Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R. & Coppola, R. Cognitive fitness of cost-efficient brain functional networks. Proc. Natl. Acad. Sci. USA 106, 11747-11752 (2009).
10. Meunier, D., Lambiotte, R. & Bullmore, E. T. Modular and hierarchically modular organization of brain networks. Front. Neurosci. 4, 200 (2010).
11. 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).
12. Stam, C. J., Jones, B. F., Nolte, G., Breakspear, M. & Scheltens, P. Small-world networks and functional connectivity in Alzheimers disease. Cereb. Cortex 17, 92-99 (2007). (Pubitemid 44973898)
13. Stam, C. J., de Haan, W., Daffertshofer, A., Jones, B. F., Manshanden, I., van Cappellen van Walsum, A. M., Montez, T., Verbunt, J. P., de Munck, J. C., van Dijk, B. W., Berendse, H. W. & Scheltens, P. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain 132, 213-224 (2009).
14. Petersen, R. C. Mild cognitive impairment as a diagnostic entity. J. Intern Med. 256, 183-194 (2004). (Pubitemid 39222988)
15. Stam, C. Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets. Physica D 163, 236-251 (2002). (Pubitemid 34201314)
16. Wang, B., Tang, H., Guo, C. &Xiu, Z. Entropy optimization of scale-free networks robustness to random failures. Physica A 363, 591-596 (2006). (Pubitemid 43375040)
17. Newman, M. E. J. Assortative mixing in networks. Phys. Rev. Lett. 89, 208701 (2002).
18. Latora, V. &Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701 (2001).
19. Humphries, M. D. & Gurney, K. Network small-world-ness: a quantitative method for determining canonical network equivalence. PLoS ONE 3, e0002051 (2008).
20. 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). (Pubitemid 35231534)
21. Cortes, C.& Vapnik, V. Support-Vector networks. Machine Learning 20, 273-297 (1995).
22. Meyer, D., Leisch, F. & Hornik, K. The support vector machine under test. Neurocomputing 55, 169-186 (2003). (Pubitemid 37336675)
23. Tan, P.-N., Steinbach, M. & Kumar, V. Introduction to Data Mining. Addison Wesley (2005).
24. Boser, B. E., Guyon, I.M. & Vapnik, V.N A training algorithm for optimal margin classifiers. Proc 5th Annual Workshop on Computational Learning Theory ACM Press, Pittsburg, PA 144-152 (1992).
25. Scholkopf, B. Support Vector Machines: A Practical Consequence of Learning Theory. IEEE Intelligent Systems 13, 18-21 (1998). (Pubitemid 128574926)
26. Minsky, M. & Papert, S. An Introduction to Computational Geometry. MIT Press (1969).
27. Domingos, P. & Pazzani, M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29, 103-137 (1997).