Science of Networks and Music: A New Approach on Musical Analysis and Creation

  • Gianfranco Campolongo
  • Stefano Vena
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


Science of Networks is a very young discipline whose results have rapidly influenced many different fields of scientific research. In this paper we present some experimentations of a new approach on generative music based on small-world networks. The basic idea of this work is that network can be a useful instrument for musical modeling, analysis and creation. We studied over 100 musical compositions of different genres (classical, pop, rock) by means of science of networks, then used this data for generating algorithms for musical creation and author attribution. The first step of this work is the implementation of a software that allows to represent and analyse musical compositions, then we developed a genetic algorithm for the production of networks with particular features. These networks are finally used for the generation of self-organized melodies and scales.


Genetic Algorithm Preferential Attachment Average Path Length Author Attribution Musical Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Watts, D.J., Strogatz, S.H.: Collective Dynamics of Small-World Networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  2. 2.
    Newman, M.E.J.: Clustering and Preferential Attachment in Networks. Phisical Review E 64, 2512 (2001)Google Scholar
  3. 3.
    Newman, M.E.J.: Models of the Small World. A Review, J. Stat. Phys. 101, 819–841 (2000)MATHCrossRefGoogle Scholar
  4. 4.
    Newman, M.E.J., Watts, D.J.: Scaling and Percolation in the Small-World Network Model. Phys. Rev. E 60, 7332 (1999)CrossRefGoogle Scholar
  5. 5.
    Albert, R., Barabási, A.L.: Statistical Mechanics of Complex Networks. Reviews of Modern Physics 74(1), 47–97 (2002)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Albert, R., Jeong, H., Barabási, A.L.: Diameter of the World Wide Web. Nature 401, 130–131 (1999)CrossRefGoogle Scholar
  7. 7.
    Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406, 378–382 (2000)CrossRefGoogle Scholar
  8. 8.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Jeong, H., Neda, Z., Barabási, A.-L.: Measuring Preferential Attachment in Evolving Networks. Europhys. Lett. 567–572 (2003)Google Scholar
  10. 10.
    Latora, V., Marchiori, M.: Efficient Behavior of Small-World Networks. The American Physical Society 87(19) (2001)Google Scholar
  11. 11.
    Latora, V., Marchiori, M.: Economic Behavior in Weighted Networks. Eur. Phys. J. B 32, 249–263 (2003)CrossRefGoogle Scholar
  12. 12.
    Marchiori, M., Latora, V.: Harmony in the Small-World. Physica A 285, 539–546 (2000)MATHCrossRefGoogle Scholar
  13. 13.
    Manaris, B., Romero, J., Machado, P., Krehbiel, D., Hirzel, T., Pharr, W., Bavis, R.B.: Zipfs Law, Music Classification and Aesthetics. Computer Music Journal 29:1, 55-69 (2005)Google Scholar
  14. 14.
    Weinberg, G.: Interconnected Musical Networks: Toward a Theoretical Framework. Computer Music Journal 29(2), 23–39 (Summer 2005)Google Scholar
  15. 15.
    Cage, J.: Silence. Wesleyan University Press, Middletown (1961)Google Scholar
  16. 16.
    Campolongo, G., Vena, S.: Complessitá e Musica. Analisi e Generazione di Melodie con Reti Small-World. In: Bertachini, P.A., Bilotta, E., Francaviglia, M., Pantano, P. (eds.) Proceedings of the Conference Mathematics, Art and Cultural Industry, Cetraro, May 19-21 (2005)Google Scholar
  17. 17.
    Campolongo, G., Vena, S.: Analysing and Creating Music through Small-World Networks. In: Proceedings of the V International Conference Understanding and Creating Music, Seconda Universitá di Napoli, Caserta, November 27-30 (2005)Google Scholar
  18. 18.
    Campolongo, G., Vena, S.: Applications of Small-World Networks to Music. In: Accepted for Generative Art Conference, Milan, December 15-17 (2005)Google Scholar
  19. 19.
    Bilotta, E., Pantano, P., Talarico, V.: Synthetic Harmonies: An Approach to Musical Semiosis By Means of Cellular Automata. In: Bedau, M.A., McCaskill, J.S., Packard, N.H., Rasmussen, S. (eds.) Artificial Life VII, August 2000. The MIT Press, Cambridge (2000)Google Scholar
  20. 20.
    Bilotta, E., Pantano, P., Talarico, V.: Music Generation Through Cellular Automata: How to Give Life to Strange Creatures. In: Proceedings of Generative Art GA 2000, Milan, Italy (2000)Google Scholar
  21. 21.
    Manaris, B., Vaughan, D., Wagner, C., Romero, N., Davis, R.B.: Evolutionary music and the zipf-mandelbrot law: Developing fitness functions for pleasant music. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 522–534. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  22. 22.
    Mandelbrot, B.: Information Theory and Psycholinguistics: a Theory of Word Frequencies. In: Lazarfeld, P., Henry, N. (eds.) Readings in Mathematical Social Science, pp. 350–368. Science Research Associates, Chicago (1966)Google Scholar
  23. 23.
    Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison-Wesley, Cambridge (1949)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gianfranco Campolongo
    • 1
  • Stefano Vena
    • 1
  1. 1.Department of LinguisticsUniversity of CalabriaArcavacata di Rende(CS)Italy

Personalised recommendations