Triadic motifs and dyadic self-organization in the World Trade Network

Abstract

In self-organizing networks, topology and dynamics coevolve in a continuous feedback, without exogenous driving. The World Trade Network (WTN) is one of the few empirically well documented examples of self-organizing networks: its topology strongly depends on the GDP of world countries, which in turn depends on the structure of trade. Therefore, understanding which are the key topological properties of the WTN that deviate from randomness provides direct empirical information about the structural effects of self-organization. Here, using an analytical pattern-detection method that we have recently proposed, we study the occurrence of triadic "motifs" (subgraphs of three vertices) in the WTN between 1950 and 2000. We find that, unlike other properties, motifs are not explained by only the in- and out-degree sequences. By contrast, they are completely explained if also the numbers of reciprocal edges are taken into account. This implies that the self-organization process underlying the evolution of the WTN is almost completely encoded into the dyadic structure, which strongly depends on reciprocity.

12 pages, 3 figures; Best Paper Award at the 6th International Conference on Self-Organizing Systems, Delft, The Netherlands, 15-16/03/2012

References in corpus (6)

• Clustering in Complex Directed Networks
• Analytical maximum-likelihood method to detect patterns in real networks
• Maximum likelihood: extracting unbiased information from complex networks
• Interplay between topology and dynamics in the World Trade Web
• Randomizing world trade. I. A binary network analysis
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Cited by in corpus (12)

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• Early-warning signals of topological collapse in interbank networks
• Randomizing bipartite networks: the case of the World Trade Web
• Reciprocity of weighted networks
• Systemic risk analysis in reconstructed economic and financial networks
• Enhanced reconstruction of weighted networks from strengths and degrees
• Unbiased sampling of network ensembles
• Reconstructing networks
• Stationarity, non-stationarity and early warning signals in economic networks
• The role of distances in the World Trade Web
• Crowded trades, market clustering, and price instability
• Economic networks in and out of equilibrium