Frank W. Takes

The Corporate Elite Community Structure of Global Capitalism

A key debate on the merits and consequences of globalisation asks to what extent we have moved to a multipolar global political economy. Here we investigate this issue through the properties and topologies of corporate elite networks and ask: what is the community structure of the global corporate elite? In order to answer this question, we analyse how the largest one million firms in the world are interconnected at the level of corporate governance through interlocking directorates. Community detection through modularity maximisation reveals that regional clusters play a fundamental role in the network architecture of the global political economy. Transatlantic connections remain particularly strong: Europe and North America remain interconnected in a dense network of shared directors. A distinct Asian cluster stands apart as separate and oriented more towards itself. While it develops and gains economic and political power, Asia remains by and large outside the scope of the networks of the incumbent global (that is, North Atlantic) corporate elite. We see this as a sign of the rise of competing corporate elites. But the corporate elites from the traditional core countries still form a powerful opponent for any competing faction in the global corporate elite.

Computing the Eccentricity Distribution of Large Graphs

The eccentricity of a node in a graph is defined as the length of a longest shortest path starting at that node. The eccentricity distribution over all nodes is a relevant descriptive property of the graph, and its extreme values allow the derivation of measures such as the radius, diameter, center and periphery of the graph. This paper describes two new methods for computing the eccentricity distribution of large graphs such as social networks, web graphs, biological networks and routing networks.We first propose an exact algorithm based on eccentricity lower and upper bounds, which achieves significant speedups compared to the straightforward algorithm when computing both the extreme values of the distribution as well as the eccentricity distribution as a whole. The second algorithm that we describe is a hybrid strategy that combines the exact approach with an efficient sampling technique in order to obtain an even larger speedup on the computation of the entire eccentricity distribution. We perform an extensive set of experiments on a number of large graphs in order to measure and compare the performance of our algorithms, and demonstrate how we can efficiently compute the eccentricity distribution of various large real-world graphs.

Determining the diameter of small world networks

In this paper we present a novel approach to determine the exact diameter (longest shortest path length) of large graphs, in particular of the nowadays frequently studied small world networks. Typical examples include social networks, gene networks, web graphs and internet topology networks. Due to complexity issues, the diameter is often calculated based on a sample of only a fraction of the nodes in the graph, or some approximation algorithm is applied. We instead propose an exact algorithm that uses various lower and upper bounds as well as effective node selection and pruning strategies in order to evaluate only the critical nodes which ultimately determine the diameter. We will show that our algorithm is able to quickly determine the exact diameter of various large datasets of small world networks with millions of nodes and hundreds of millions of links, whereas before only approximations could be given.

Where is the global corporate elite? A large-scale network study of local and nonlocal interlocking directorates

Business elites reconfigure their locus of organization over time, from the city level, to the national level, and beyond. We ask what the current level of elite organization is and propose a novel theoretical and empirical approach to answer this question. Building on the universal distinction between local and nonlocal ties we use network analysis and community detection to dissect the global network of interlocking directorates among over five million firms. We find that elite orientation is indeed changing from the national to the transnational plane, but we register a considerable heterogeneity across different regions in the world. In some regions the business communities are organized along national borders, whereas in other areas the locus of organization is at the city level or international level. London dominates the global corporate elite network. Our findings underscore that the study of corporate elites requires an approach that is sensitive to levels of organization that go beyond the confines of nation states.

Uncovering Offshore Financial Centers: Conduits and Sinks in the Global Corporate Ownership Network

Multinational corporations use highly complex structures of parents and subsidiaries to organize their operations and ownership. Offshore Financial Centers (OFCs) facilitate these structures through low taxation and lenient regulation, but are increasingly under scrutiny, for instance for enabling tax avoidance. Therefore, the identification of OFC jurisdictions has become a politicized and contested issue. We introduce a novel data-driven approach for identifying OFCs based on the global corporate ownership network, in which over 98 million firms (nodes) are connected through 71 million ownership relations. This granular firm-level network data uniquely allows identifying both sink-OFCs and conduit-OFCs. Sink-OFCs attract and retain foreign capital while conduit-OFCs are attractive intermediate destinations in the routing of international investments and enable the transfer of capital without taxation. We identify 24 sink-OFCs. In addition, a small set of five countries – the Netherlands, the United Kingdom, Ireland, Singapore and Switzerland – canalize the majority of corporate offshore investment as conduit-OFCs. Each conduit jurisdiction is specialized in a geographical area and there is significant specialization based on industrial sectors. Against the idea of OFCs as exotic small islands that cannot be regulated, we show that many sink and conduit-OFCs are highly developed countries.

Centrality in the global network of corporate control

Corporations across the world are highly interconnected in a large global network of corporate control. This paper investigates the global board interlock network, covering 400,000 firms linked through 1,700,000 edges representing shared directors between these firms. The main focus is on the concept of centrality, which is used to investigate the embeddedness of firms from a particular country within the global network. The study results in three contributions. First, to the best of our knowledge for the first time we can investigate the topology as well as the concept of centrality in corporate networks at a global scale, allowing for the largest cross-country comparison ever done in interlocking directorates literature. We demonstrate, among other things, extremely similar network topologies, yet large differences between countries when it comes to the relation between economic prominence indicators and firm centrality. Second, we introduce two new metrics that are specifically suitable for comparing the centrality ranking of a partition to that of the full network. Using the notion of centrality persistence we propose to measure the persistence of a partition’s centrality ranking in the full network. In the board interlock network, it allows us to assess the extent to which the footprint of a national network is still present within the global network. Next, the measure of centrality ranking dominance tells us whether a partition (country) is more dominant at the top or the bottom of the centrality ranking of the full (global) network. Finally, comparing these two new measures of persistence and dominance between different countries allows us to classify these countries based the their embeddedness, measured using the relation between the centrality of a country’s firms on the national and the global scale of the board interlock network.

Fast Diameter Computation of Large Sparse Graphs Using GPUs

In this paper we propose a highly parallel GPU-based bounding algorithm for computing the exact diameter of large real-world sparse graphs. The diameter is defined as the length of the longest shortest path between vertices in the graph, and serves as a relevant property of all types of graphs that are nowadays frequently studied. Examples include social networks, webgraphs and routing networks. We verify the performance of our parallel approach on a set of large graphs comprised of millions of vertices, and using a CUDA GPU observe an increase in performance of up to 21.1x compared to a CPU algorithm using the same strategy. Based on these results, we provide a characterization of the types of graphs that are well-suited for traversal by means of our parallel diameter algorithm. We furthermore include a comparison of different GPU algorithms for single-source shortest path computations, which is not only a crucial step in computing the diameter, but also relevant in many other distance and neighborhood-based algorithms.

The effects of data quality on the analysis of corporate board interlock networks

Nowadays, social networks of ever increasing size are studied by researchers from a range of disciplines. The data underlying these networks is often automatically gathered from APIs, websites or existing databases. As a result, the quality of this data is typically not manually validated, and the resulting networks may be based on false, biased or incomplete data. In this paper, we investigate the effect of data quality issues on the analysis of large networks. We focus on the global board interlock network, in which nodes represent firms across the globe, and edges model social ties between firms -- shared board members holding a position at both firms. First, we demonstrate how we can automatically assess the completeness of a large dataset of 160 million firms, in which data is missing not at random. Second, we present a novel method to increase the accuracy of the entries in our data. By comparing the expected and empirical characteristics of the resulting network topology, we develop a technique that automatically prunes and merges duplicate nodes and edges. Third, we use a case study of the board interlock network of Sweden to show how poor quality data results in incorrect network topologies, biased centrality values and abnormal influence spread under a well-known diffusion model. Finally, we demonstrate how our data quality assessment methods help restore the correct network structure, ultimately allowing us to derive meaningful and correct results from analyzing the network.

On the Solvability of the Six Degrees of Kevin Bacon Game

In this paper, we will propose a new algorithm that computes the radius and the diameter of a graph G = (V,E), by finding bounds through heuristics and improving them until exact values can be guaranteed. Although the worst-case running time is \(\mathcal{O}(|V|\cdot |E|)\), we will experimentally show that, in the case of real-world networks, it performs much better, finding the correct radius and diameter value after 10–100 BFSes instead of |V| BFSes (independent of the value of |V|), and thus having running time \(\mathcal{O}(|E|)\). Apart from efficiency, compared to other similar methods, the one proposed in this paper has three other advantages. It is more robust (even in the worst cases, the number of BFSes performed is not very high), it is able to simultaneously compute radius and diameter (halving the total running time whenever both values are needed), and it works both on directed and undirected graphs with very few modifications. As an application example, we use our new algorithm in order to determine the solvability over time of the “six degrees of Kevin Bacon” game.

Adaptive Landmark Selection Strategies for Fast Shortest Path Computation in Large Real-World Graphs

This paper considers the task of answering shortest path queries in large real-world graphs such as social networks, communication networks and web graphs. The traditional Breadth First Search (BFS) approach for solving this problem is too time-consuming when networks with millions of nodes and possibly billions of edges are considered. A common technique to address these complexity issues uses a small set of landmark nodes from which the distance to all other nodes is precomputed in order to then answer arbitrary distance queries by navigating via one of the selected landmarks. Although many strategies to select landmarks have been introduced in previous work, the problem of finding an optimal set that covers the entire graph remains NP-hard. Our contribution starts with a study of characteristics that determine the successfulness of a landmark selection strategy. We propose a new adaptive heuristic for selecting landmarks that does not only pick central nodes, but also ensures that these landmarks properly cover different areas of the graph. Experiments on a diverse set of large graphs show that the proposed selection strategy and assisting node processing technique can efficiently estimate the node-to-node distance in graphs with millions of nodes with very high accuracy, while using the same amount of precomputation time as previously proposed strategies.