Abstract
Ownership relationships between legal entities can be represented as a large directed and weighted graph. This paper provides a methodology and an empirical analysis of such network, composed of millions of nodes and edges. To do so, we employ a variety of metrics from graph analytics and algorithms from influence maximization (IM). For reasons of confidentiality, our empirical analysis is carried out on aggregation at country and sector level, analysing in details the case of France. Our results offer new type of intuitions and metrics in this area by highlighting the existence of strong communities of capitalistic property. Finally, we discuss influence maximization methods as means to evaluate an entity impact in the socialistic graph.
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Khalife, S., Read, J., Vazirgiannis, M. (2020). Empirical Analysis of a Global Capital-Ownership Network. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_53
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DOI: https://doi.org/10.1007/978-3-030-36683-4_53
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