MULTI-WEIGHTED MONETARY TRANSACTION NETWORK

H. Wang (), E. van Boven (), A. Krishnakumar, M. Hosseini, H. van Hooff, T. Takema, N. Baken and P. van Mieghem
Additional contact information
H. Wang: Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, P. O. Box 5031, 2600 GA Delft, The Netherlands
E. van Boven: Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, P. O. Box 5031, 2600 GA Delft, The Netherlands;
A. Krishnakumar: KPN Royal, P. O. Box 30.000, 2500 GA The Hague, The Netherlands
M. Hosseini: Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, P. O. Box 5031, 2600 GA Delft, The Netherlands;
H. van Hooff: Statistics Netherlands, P. O. Box 24.500, 2490 HA, The Hague, The Netherlands
T. Takema: Statistics Netherlands, P. O. Box 24.500, 2490 HA, The Hague, The Netherlands
N. Baken: Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, P. O. Box 5031, 2600 GA Delft, The Netherlands;
P. van Mieghem: Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, P. O. Box 5031, 2600 GA Delft, The Netherlands

Advances in Complex Systems (ACS), 2011, vol. 14, issue 05, 691-710

Abstract: This paper aims to both develop and apply advances from the field of complex networks to large economic systems and explore the (dis)similarities between economic systems and other real-world complex networks. For the first time, the nature and evolution of the Dutch economy are captured by means of a data set analysis that describes the monetary transactions among 105 economical activity clusters over the period 1987–2007. We propose to represent this data set as a multi-weighted network, called the monetary transaction network. Each node represents a unique activity cluster. Nodes are interconnected via monetary transactions. The millions of euros that traverse the links and that circulate inside each activity cluster are denoted by a link weight and a node weight respectively. By applying innovative methodologies from network theory, we observe important features of the monetary transaction network as well as its evolution: (a) Activity clusters with a large internal flow tend to cooperate with many other clusters via high volume monetary transactions. (b) Activity clusters with a lower internal transaction volume prefer to transact with fewer neighboring nodes that have a higher internal flow. (c) The node weights seem to follow a power law distribution. Surprisingly, (b) and (c) have been observed in community structures of many real-world networks as well. (d) Activity clusters tend to balance the monetary volume of their transactions with their neighbors, reflected by a positive link weight correlation around each node. This correlation becomes stronger over time while the number of links increases over time as well.

Keywords: Complex network; monetary transaction network; weighted network; mode weight (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S021952591100330X

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