Advertisement

The Explanatory Power of Relations and an Application to an Economic Network

  • Mauricio Monsalve
Part of the Studies in Computational Intelligence book series (SCI, volume 424)

Abstract

Understanding the topology of complex networks is a central concern of network science. Within this endeavor, we study the problems of building theories from the non topological attributes of linked vertices and assessing their explanatory power. We design a simple framework for building theories from the attributes of vertices and apply it to explain the topology of the Chilean shareholding network, an economic network which vertices represent firms and edges represent an ownership relation, finding that a relational theory based on financial information explained the topology of the network only in part.

Keywords

Directed Graph Degree Distribution Undirected Graph Topological Attribute Directed Edge 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barabasi, A.L.: Scale-free networks: a decade and beyond. Science 325(5939), 412–413 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explor. Newsl. 7(2), 3–12 (2005)CrossRefGoogle Scholar
  4. 4.
    Kenny, D.A., Kashy, D.A., Cook, W.L.: Dyadic data analysis. The Guilford Press, NY (2006)Google Scholar
  5. 5.
    Mizruchi, M.S., Marquis, C.: Egocentric, sociocentric or dyadic? Identifying the appropriate level of analysis in the study of organizational networks. Soc. Netw. 28(3), 187–208 (2006)CrossRefGoogle Scholar
  6. 6.
    Domingos, P.: Prospects and challenges for multi-relational data mining. SIGKDD Explor. Newsl. 5(1), 80–83 (2003)CrossRefGoogle Scholar
  7. 7.
    Getoor, L.: Link mining: a new data mining challenge. SIGKDD Explor. Newsl. 5(1), 84–89 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Popescul, A., Popescul, R., Ungar, L.H.: Statistical relational learning for link prediction. In: Proc. of the Workshop Learn Stat Model Relat Data, IJCAI (2003)Google Scholar
  9. 9.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a Feather: Homophily in Social Networks. Annual Rev. Sociol. 27, 415–444 (2001)CrossRefGoogle Scholar
  10. 10.
    Patil, A.N.: Homophily based link prediction in social networks. Tech paper. Stony Brook (2009)Google Scholar
  11. 11.
    Newman, M.E.J.: Assortative Mixing in Networks. Phys. Rev. Lett. 89, 208701 (2002)CrossRefGoogle Scholar
  12. 12.
    Garlaschelli, D., Battiston, S., Castri, M., Servedio, V.D.P., Caldarelli, G.: The scale-free topology of market investments. Physica A: Stat. Mech. Appl. 350(2-4), 491–499 (2005)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Battiston, S., Glattfelder, J.B., Garlaschelli, D., Lillo, F., Caldarelli, G.: The Structure of Financial Networks. In: Estrada, E., Fox, M., Higham, D.J., Oppo, G.-L. (eds.) Network Science: Complexity in Nature and Technology, pp. 131–163. Springer, London (2010)Google Scholar
  14. 14.
    Scher, M.: Bank-firm Cross-shareholding in Japan: What is it, why does it matter, is it winding down? DESA Discussion Paper No. 15. ST/ESA/1999/DP.15. United Nations (2001)Google Scholar
  15. 15.
    Souma, W., Fujiwara, Y., Aoyama, H.: Heterogeneous Economic Networks. In: Namatame, A., et al. (eds.) Proc. of the Workshop on Economics and Heterogeneous Interacting Agents. Springer, Tokyo (2005)Google Scholar
  16. 16.
    Caldarelli, G., Battiston, S., Garlaschelli, D., Catanzaro, M.: Emergence of Complexity in Financial Networks. In: Ben-Naim, E., Frauenfelder, H., Toroczkai, Z. (eds.) Complex Networks. Lect. Notes Phys., vol. 650, pp. 399–423. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Piccardi, C., Calatroni, L., Bertoni, F.: Communities in Italian corporate networks. Physica A 389, 5247–5258 (2010)CrossRefGoogle Scholar
  18. 18.
    Schweitzer, F., Fagiolo, G., Sornette, D., Vega-Redondo, F., Vespignani, A., White, D.R.: Economic Networks: The New Challenges. Science 325(5939), 422–442 (2009)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Köbler, J., Schöning, U., Torán, J.: Graph isomorphism is low for PP. Comput. Complexity 2, 301–330 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Bunke, H., Foggia, P., Guidobaldi, C., Sansone, C., Vento, M.: A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 123–132. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Tian, Y., Patel, J.M.: TALE: A Tool for Approximate Large Graph Matching. In: Proc. of the IEEE, 24th ICDE, pp. 963–972 (2008)Google Scholar
  22. 22.
    Spielman, D.A.: Spectral Graph Theory and its Applications. In: Proc. of the FOCS, pp. 29–38 (2007)Google Scholar
  23. 23.
    Borgatti, S.P., Carley, K.M., Krackhardt, D.: On the robustness of centrality measures under conditions of imperfect data. Soc. Netw. 28(2), 124–136 (2006)CrossRefGoogle Scholar
  24. 24.
    Newman, M.E.J.: A measure of betweenness centrality based on random walks. Soc. Netw. 27(1), 39–54 (2005)CrossRefGoogle Scholar
  25. 25.
    Jacob, R., Koschützki, D., Lehmann, K.A., Peeters, L., Tenfelde-Podehl, D.: Algorithms for Centrality Indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, pp. 62–82. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  26. 26.
    Turlach, B.A.: Bandwidth selection in kernel density estimation: a rewiew. CORE and Institut de Statistique, 23–493 (1993)Google Scholar
  27. 27.
    Superintendencia de Valores y Seguros, http://www.svs.gob.cl (last accessed: November 10, 2011)
  28. 28.
    Monsalve, M.: A study of the structure and dynamics of the Chilean shareholding network. Dissertation, Universidad de Chile (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.The University of IowaIowa CityUSA

Personalised recommendations