Detecting Motifs in Multiplex Corporate Networks

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)


The main topic of this paper is the discovery of motifs in multiplex corporate networks. Network motifs are small subgraphs occurring at significantly higher numbers than in similar random networks. They can be seen as the building blocks of a complex network. In real-world network data, multiple types of (possibly overlapping) relationships may be present among the nodes, forming so-called multiplex networks. Detecting motifs in such networks is difficult, as existing subgraph enumeration algorithms are not directly applicable to multiplex network data. In addition, the selection of a proper multiplex null model to test the significance of the enumerated subgraphs is nontrivial. This paper addresses these two problems, resulting in three contributions. First, we present a method based on layer encoding for adequately handling the multiplex aspect in subgraph enumeration. Second, a null model is proposed that is able to preserve the relationship between the different types of links, taking into account that a particular link type may be the result of a projection from a bipartite network. Finally, we perform experiments on corporate network data from Germany, in which around \(75\,000\) nodes represent corporations and roughly \(195\,000\) links represent connectedness of firms based on shared board members and ownership. We demonstrate how incorporating the multiplex aspect in motif detection is able to reveal new insights that could not be obtained by studying only one type of relationship. Furthermore, results uncover how the financial sector is over-represented in the more complex motifs, hinting at a surprisingly prominent role of the financial sector in the largely industry-oriented corporate network of Germany.



The first author is supported by funding from the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (grant agreement 638946). Thanks to the CORPNET group ( for useful suggestions.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.CORPNET Research Group (AISSR)University of AmsterdamAmsterdamNetherlands
  2. 2.Leiden Institute of Advanced Computer Science (LIACS)Leiden UniversityLeidenNetherlands

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