2012/01/01 by Tiziano Squartini, Diego Garlaschelli · 33 citations
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Economic and Technological Innovation #Network topology #Process (computing) #Randomness #Reciprocal #Topology (electrical circuits) #World trade #cs.SI #physics.data-an #physics.soc-ph #q-fin.GN
paper · pdf · doi:10.1007/978-3-642-28583-7_3
published in Lecture notes in computer science, 24-35 (Springer Science+Business Media) · 12 pages, 3 figures; Best Paper Award at the 6th International Conference on Self-Organizing Systems, Delft, The Netherlands, 15-16/03/2012
openalex publication_date 2012/01/01 · arxiv created 2012/01/10 · arxiv updated 2014/01/14 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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.