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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Barabasi, A.L.: Scale-free networks: a decade and beyond. Science 325(5939), 412–413 (2009)
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explor. Newsl. 7(2), 3–12 (2005)
Kenny, D.A., Kashy, D.A., Cook, W.L.: Dyadic data analysis. The Guilford Press, NY (2006)
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)
Domingos, P.: Prospects and challenges for multi-relational data mining. SIGKDD Explor. Newsl. 5(1), 80–83 (2003)
Getoor, L.: Link mining: a new data mining challenge. SIGKDD Explor. Newsl. 5(1), 84–89 (2003)
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)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a Feather: Homophily in Social Networks. Annual Rev. Sociol. 27, 415–444 (2001)
Patil, A.N.: Homophily based link prediction in social networks. Tech paper. Stony Brook (2009)
Newman, M.E.J.: Assortative Mixing in Networks. Phys. Rev. Lett. 89, 208701 (2002)
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)
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)
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)
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)
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)
Piccardi, C., Calatroni, L., Bertoni, F.: Communities in Italian corporate networks. Physica A 389, 5247–5258 (2010)
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)
Köbler, J., Schöning, U., Torán, J.: Graph isomorphism is low for PP. Comput. Complexity 2, 301–330 (1992)
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)
Tian, Y., Patel, J.M.: TALE: A Tool for Approximate Large Graph Matching. In: Proc. of the IEEE, 24th ICDE, pp. 963–972 (2008)
Spielman, D.A.: Spectral Graph Theory and its Applications. In: Proc. of the FOCS, pp. 29–38 (2007)
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)
Newman, M.E.J.: A measure of betweenness centrality based on random walks. Soc. Netw. 27(1), 39–54 (2005)
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)
Turlach, B.A.: Bandwidth selection in kernel density estimation: a rewiew. CORE and Institut de Statistique, 23–493 (1993)
Superintendencia de Valores y Seguros, http://www.svs.gob.cl (last accessed: November 10, 2011)
Monsalve, M.: A study of the structure and dynamics of the Chilean shareholding network. Dissertation, Universidad de Chile (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Monsalve, M. (2013). The Explanatory Power of Relations and an Application to an Economic Network. In: Menezes, R., Evsukoff, A., González, M. (eds) Complex Networks. Studies in Computational Intelligence, vol 424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30287-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-30287-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30286-2
Online ISBN: 978-3-642-30287-9
eBook Packages: EngineeringEngineering (R0)