Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


Preferences help
enabled [disable] Abstract
Number of results
2008 | 114 | 3 | 491-499

Article title

Cluster Expansion Method for Evolving Weighted Networks Having Vector-Like Nodes

Authors

Content

Title variants

Languages of publication

EN

Abstracts

EN
The cluster variation method known in statistical mechanics and condensed matter is revived for weighted bipartite networks. The decomposition (or expansion) of a Hamiltonian through a finite number of components, whence serving to define variable clusters, is recalled. As an illustration the network built from data representing correlations between (4) macroeconomic features, i.e. the so-called vector components, of 15 EU countries, as (function) nodes, is discussed. We show that statistical physics principles, like the maximum entropy criterion points to clusters, here in a (4) variable phase space: Gross Domestic Product, Final Consumption Expenditure, Gross Capital Formation and Net Exports. It is observed that the maximum entropy corresponds to a cluster which does not explicitly include the Gross Domestic Product but only the other (3) "axes", i.e. consumption, investment and trade components. On the other hand, the minimal entropy clustering scheme is obtained from a coupling necessarily including Gross Domestic Product and Final Consumption Expenditure. The results confirm intuitive economic theory and practice expectations at least as regards geographical connexions. The technique can of course be applied to many other cases in the physics of socio-economy networks.

Keywords

Contributors

author
  • GRAPES, SUPRATECS, U.Lg, B5a Sart-Tilman, B-4000 Liège, Belgium
author
  • National College Roman, Voda Roman-5550, Neamt, Romania

References

  • 1. R. Albert, A.-L. Barabasi, Rev. Mod. Phys. 74, 47 (2002)
  • 2. S.N. Dorogovtsev, J.F.F. Mendes, Evolution of Networks: From Biological Nets to the Internet and WWW, Oxford Univ. Press, Oxford 2003
  • 3. R. Kikuchi, Phys. Rev. 81, 988 (1951)
  • 4. M. Kurata, R. Kikuchi, T. Watari, J. Chem. Phys. 21, 434 (1953)
  • 5. R. Kikuchi, S.G. Brush, J. Chem. Phys. 47, 195 (1967)
  • 6. A. Pelizzola, J. Phys. A 38, R309 (2005)
  • 7. G. Biroli, O. Parcollet, G. Kotliar, Phys. Rev. B 69, 205108 (2004)
  • 8. P. Smyth, Pattern Recogn. Lett. 18, 1261 (1997)
  • 9. S.N. Durlauf, D.T. Quah, in: Handbook of Macroeconomics, Eds. J.B. Taylor, M. Woodford, North-Holland Elsevier Sci., Dordrecht 1999, p. 231
  • 10. http://helpdesk.rootsweb.com/codes/
  • 11. http://devdata.worldbank.org/query/default.htm
  • 12. J. Miskiewicz, M. Ausloos, Int. J. Mod. Phys. C 17, 317 (2006)
  • 13. M. Ausloos, R. Lambiotte, Physica A 382, 16 (2007)
  • 14. M. Gligor, M. Ausloos, J. Econ. Integration 23, 297 (2008)
  • 15. R.N. Mantegna, Eur. Phys. J. B 11, 193 (1999)
  • 16. M. Gligor, M. Ausloos, Eur. Phys. J. B 57, 139 (2007)
  • 17. D.J. Watts, S.H. Strogatz, Nature 393, 440 (1998)
  • 18. R. Pastor-Satorras, A. Vespignani, Evolution and Structure of the Internet: A Statistical Physics Approach, Cambridge University Press, Cambridge 2004;R. Pastor-Satorras, M. Rubi, A. Diaz-Guilera, Statistical Mechanics of Complex Networks, Lect. Notes Phys., Vol. 625, Springer, Berlin 2003
  • 19. T. Mora, Appl. Econ. Lett. 12, 937 (2005)
  • 20. S. Barrios, E. Strobl, Econ. Lett. 82, 71 (2004)

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.bwnjournal-article-appv114n301kz
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.