Abstract
In this paper, we investigate the network of ownership relationships among European firms and its embedding in the geographical space. We carry out a detailed analysis of geographical distances between pairs of nodes, connected by edges or by shortest paths of varying length. In particular, we study the relation between geographical distance and network distance in comparison with a random spatial network model. While the distribution of geographical distance can be fairly well reproduced, important deviations appear in the network distance and in the size of the largest strongly connected component. Our results show that geographical factors allow us to capture several features of the network, while the deviations quantify the effect of additional economic factors at work in shaping the topology. The analysis is relevant to other types of geographically embedded networks and sheds light on the link formation process in the presence of spatial constraints.
Highlights
Among the domains in which geographically embedded networks have been investigated, the case of economic networks has so far received very little attention
We investigate the network of ownership relationships among European firms and its embedding in the geographical space
We first verify that the European Ownership Network is a small world (SW); we investigate how this property interacts with geographical distance
Summary
Throughout the paper, we aim at assessing the extent to which the empirical properties of the European ownership network are consistent with those of a network obtained from a random link formation process. As in the Maslov–Sneppen algorithm [26,27,28], the basic idea is to choose random pairs of links and swap the sources of the two links. This automatically satisfies constraints 1–2, provided that each new link of the pair is accepted if and only if: (i) it is not a loop—the no-loop condition; and (ii) it does not already exist—the no-multiple-links condition. Sources with large out-degree have higher probability of being selected, because they occur in several links We use both degree and distance as input to determine the properties they induce in terms of: (1) geographical distance between higher-order neighbours, (2) small world and (3) connected components
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