Janós Kertész quantifies social contacts


Apr 3, 2017

Quantifying Social Contacts

 

Janós Kertész, External Faculty member of the CSH Vienna,  Professor at Central European University Budapest, and member of the Hungarian Academy of Sciences, did not only receive extensive media coverage these days in the Austrian daily Der Standard, April 5, but also held a lecture about the Multiplex Modeling of the Society.

 

Did you know that social interaction is not free of scale?

Janós stated that the society has a multi-layered structure, where the layers represent the different contexts resulting in a community structure with strong overlaps. To model this structure he and his team began with a single-layer weighted social network (WSN) model showing the Granovetterian correlations between link strength and topology. They found that when merging such WSN models, a sufficient amount of inter-layer correlation is needed to maintain these correlations, but they destroy the enhancement in the community overlap due to multiple layers. To resolve this, the scientists devised a geographic multi-layer WSN model, where the indirect inter-layer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure was preserved.

 

The network of social interactions can also be considered as a multiplex from another point of view: each layer corresponds to one communication channel and the aggregate of all them constitutes the entire social network. However, usually one has information only about one of the channels, which should be considered as a sample of the whole. Illustrated by simulations and analytical methods, Janós explained that this sampling may lead to bias. “For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get with reasonable assumptions about the sampling process a monotonously decreasing distribution as observed in empirical studies of single channel data.”

 

During the talk he analyzed the far-reaching consequences of his team’s findings.


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