The Multilayer Structure of Corporate Networks
arXiv:1901.07411 · doi:10.1088/1367-2630/ab022d
Abstract
Various company interactions can be described by networks, for instance the ownership networks and the board membership networks. To understand the ecosystem of companies, these interactions cannot be seen in isolation. For this purpose we construct a new multilayer network of interactions between companies in Germany and in the United Kingdom, combining ownership links, social ties through joint board directors, R\&D collaborations and stock correlations in one linked multiplex dataset. We describe the features of this network and show there exists a non-trivial overlap between these different types of networks, where the different types of connections complement each other and make the overall structure more complex. This highlights that corporate control, boardroom influence and other connections have different structures and together make an even smaller corporate world than previously reported. We have a first look at the relation between company performance and location in the network structure.
arXiv comment14 pages, 5 figures, 3 tables
References in corpus (14)
- Multilayer Networks
- The structure and dynamics of multilayer networks
- Hierarchical Structure in Financial Markets
- A tool for filtering information in complex systems
- The network of global corporate control
- Layer aggregation and reducibility of multilayer interconnected networks
- Networks of equities in financial markets
- MuxViz: A Tool for Multilayer Analysis and Visualization of Networks
- The scale-free topology of market investments
- Early-warning signals of topological collapse in interbank networks
- Centralities of Nodes and Influences of Layers in Large Multiplex Networks
- Unfolding the complexity of the global value chain: Strengths and entropy in the single-layer, multiplex, and multi-layer international trade networks
- The Rise and Fall of R&D Networks
- Influence of technological innovations on industrial production: A motif analysis on the multilayer network