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)


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.


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.The University of IowaIowa CityUSA

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