Understanding complex relationships: Scientists from Freiburg show how global properties of networks become apparent in local characteristics

In an article appearing in the scientific journal PLoS ONE, Stefano Cardanobile and colleagues describe how they analysed 200,000 networks which they generated in a computer – using models that are employed by scientists to understand the properties of naturally occurring networks. The researchers compared the results obtained from these models with well-understood networks from the real world: the metabolism of a bacterium, the relationship of synonyms in a thesaurus, and the nervous system of a worm. Thus, they were able to assess which model networks can predict the behaviour of its real-life counterpart the best. These insights can help colleagues from other fields to choose the right model in their specific research.

Most importantly, the scientists from Freiburg could demonstrate that it is possible to draw conclusions about global properties of complex networks from local statistical data. This means that one can discover important properties of networks even if they are not completely analysed – very often an impossible task in large systems such as human social contacts or connections in the brain. Therefore, the authors see their study to represent an important step towards a better understanding of complex networks.

Statistical analyses allow to compare natural networks like the nervous system of a worm (top) with theoretical model networks (bottom). Image: Bernstein Center Freiburg; worm network after Varshney et al., PLoS Comp Biol. 2011.

Cardanobile S, Pernice V, Deger M and Rotter S (2012) Inferring general relations between network characteristics from specific network ensembles. PLoS ONE, http://dx.plos.org/10.1371/journal.pone.0037911
 

Press release by the University of Freiburg (English/German)