On using centrality to understand importance of entities in the Panama Papers

Abstract

The Panama Papers comprise one of the most recent influential leaks containing detailed information on intermediary companies (such as law firms), offshore entities and company officers, and serve as a valuable source of insight into the operations of (approximately) 214,000 shell companies incorporated in tax havens around the globe over the past half century. Entities and relations in the papers can be used to construct a network that permits, in principle, a systematic and scientific study at scale using techniques developed in the computational social science and network science communities. In this paper, we propose such a study by attempting to quantify and profile the importance of entities. In particular, our research explores whether intermediaries are significantly more influential than offshore entities, and whether different centrality measures lead to varying, or even incompatible, conclusions. Some findings yield conclusions that resemble Simpson’s paradox. We also explore the role that jurisdictions play in determining entity importance.

Knowledge Graphs: A Practical Review of the Research Landscape

“…More recently, within AI, graphs have found use in sub-fields ranging from planning to probabilistic reasoning and inference [2,3]. In the computational social sciences [4], as well as in complex systems research in the physical sciences, networks have emerged as an important model, with many important findings over the years [5][6][7][8], including domainspecific applications in finance [9][10][11][12], crisis informatics [13,14], biology [15][16][17] and even human trafficking [18][19][20][21]. Of course, these models are not exclusive to the communities where they are most prominent, although the goals of each community tend to be more distinct.…”

Special Issue on Artificial Intelligence and Complex Systems

“…The discussion in the paper also suggests that context is fundamentally necessary to understand complex systems using AI methodologies. Such systems are increasingly being used to address problems ranging from 'social good' problems, such as human trafficking [6][7][8], crisis informatics [9][10][11], and illicit finance [12][13][14], to more commercial and industrial problems, such as healthcare and e-commerce [15][16][17][18][19].…”