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Register a new user{alpha}-Indirect Control in Onion-like Networks
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Tens of thousands of parent companies control millions of subsidiaries through long chains of intermediary entities in global corporate networks. Conversely, tens of millions of entities are directly held by sole owners. We propose an algorithm for identification of ultimate controlling entities in such networks that unifies direct and indirect control and allows for continuous interpolation between the two concepts via a factor damping long paths. By exploiting onion-like properties of ownership networks the algorithm scales linearly with the network size and handles circular ownership by design. We apply it to the universe of 4.2 mln UK companies and 4 mln of their holders to understand the distribution of control in the country. Furthermore, we provide the first independent evaluation of the control identification results. We reveal that the proposed $alpha$-ICON algorithm identifies more than 96% of beneficiary entities from the evaluation set and supersedes the existing approaches reported in the literature. We refer the superiority of $alpha$-ICON algorithm to its ability to correctly identify the parents long away from their subsidiaries in the network.
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We report electrical conductivity measurements of Polymethyl-methacrylate filled by onion-like carbon particles with primary particle size of $approx 5$ nm. We shown that the conductivity $sigma$ is exceptionally high even at very low loadings, and that its low-temperature dependence follows a Coulomb gap regime at atmospheric pressure and an activated behavior at a pressure of $2$ GPa. We interpret this finding in terms of the enhancement under the applied pressure of the effective dielectric permittivity within the aggregates of onion-like carbons, which improves the screening of the Coulomb interaction and reduces the optimal hopping distance of the electrons.
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