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.

翻译：数以万计的母公司通过全球公司网络的中转实体的长链控制着数百万个子公司。相反,数以百万计的实体直接由独资拥有。我们建议一种算法,用以确定这种网络中最终控制实体,这种控制实体能够统一直接和间接控制,并通过一个阻隔长路的因素允许这两个概念之间不断的相互交错。通过利用所有权网络的类似洋葱特性,算法以网络规模线性标尺处理循环所有权。我们将其应用于4.2毫升英国公司及其4毫升持有者,以了解国内控制权的分配情况。此外,我们提供了对控制识别结果的第一次独立评价。我们透露,拟议的alpha$-ICON算法确定了评价组96%以上的受益实体,并取代了文献中报告的现有方法。我们把“alpha$-ICON算法”的优势指它能够正确识别远离网络子公司的父母。