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

Algorithms for Analyzing and Mining Real-World Graphs

This thesis is about algorithms for analyzing large real-world graphs (or networks). Examples include (online) social networks, webgraphs, information networks, biological networks and scientific collaboration and citation networks. Although these graphs differ in terms of what kind of information the objects and relationships represent, it turns out that the structure of each these networks is surprisingly similar.For computer scientists, there is an obvious challenge to design efficient algorithms that allow large graphs to be processed and analyzed in a practical setting, facing the challenges of processing millions of nodes and billions of edges. Specifically, there is an opportunity to exploit the non-random structure of real-world graphs to efficiently compute or approximate various properties and measures that would be too hard to compute using traditional graph algorithms. Examples include computation of node-to-node distances and extreme distance measures such as the exact diameter and radius of a graph.NWOAlgorithms and the Foundations of Software technolog

Trajectories through temporal networks

What do football passes and financial transactions have in common? Both are networked walk processes that we can observe, where records take the form of timestamped events that move something tangible from one node to another. Here we propose an approach to analyze this type of data that extracts the actual trajectories taken by the tangible items involved. The main advantage of analyzing the resulting trajectories compared to using, e.g., existing temporal network analysis techniques, is that sequential, temporal, and domain-specific aspects of the process are respected and retained. As a result, the approach lets us produce contextually-relevant insights. Demonstrating the usefulness of this technique, we consider passing play within association football matches (an unweighted process) and e-money transacted within a mobile money system (a weighted process). Proponents and providers of mobile money care to know how these systems are used-using trajectory extraction we find that 73% of e-money was used for stand-alone tasks and only 21.7% of account holders built up substantial savings at some point during a 6-month period. Coaches of football teams and sports analysts are interested in strategies of play that are advantageous. Trajectory extraction allows us to replicate classic results from sports science on data from the 2018 FIFA World Cup. Moreover, we are able to distinguish teams that consistently exhibited complex, multi-player dynamics of play during the 2017-2018 club season using ball passing trajectories, coincidentally identifying the winners of the five most competitive first-tier domestic leagues in Europe.Algorithms and the Foundations of Software technolog

Efficiently Counting Complex Multilayer Temporal Motifs in Large-Scale Networks

This paper proposes novel algorithms for efficiently counting complex network motifs in dynamic networks that are changing over time. Network motifs are small characteristic configurations of a few nodes and edges, and have repeatedly been shown to provide insightful information for understanding the meso-level structure of a network. Here, we deal with counting more complex temporal motifs in large-scale networks that may consist of millions of nodes and edges. The first contribution is an efficient approach to count temporal motifs in multilayer networks and networks with partial timing, two prevalent aspects of many real-world complex networks. We analyze the complexity of these algorithms and empirically validate their performance on a number of real-world user communication networks extracted from online knowledge exchange platforms. Among other things, we find that the multilayer aspects provide significant insights in how complex user interaction patterns differ substantially between online platforms. The second contribution is an analysis of the viability of motif counting algorithms for motifs that are larger than the triad motifs studied in previous work. We provide a novel categorization of motifs of size four, and determine how and at what computational cost these motifs can still be counted efficiently. In doing so, we delineate the “computational frontier” of temporal motif counting algorithms.Algorithms and the Foundations of Software technolog