How Physical Proximity Shapes Complex Social Networks. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Stopczynski, Arkadiusz; Pentland, Alex; Lehmann, Sune
• Is Part Of: Scientific reports. Volume 8:Issue 1(2018)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-018-36116-6
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract Social interactions among humans create complex networks and – despite a recent increase of online communication – the interactions mediated through physical proximity remain a fundamental way for people to connect. A common way to quantify the nature of the links between individuals is to consider repeated interactions: frequently occurring interactions indicate strong ties, such as friendships, while ties with low weights can indicate random encounters. Here we focus on a different dimension: rather than the strength of links, we study physical distance between individuals when a link is activated. The findings presented here are based on a dataset of proximity events in a population of approximately 500 individuals. To quantify the impact of the physical proximity on the dynamic network, we use a simulated epidemic spreading processes in two distinct networks of physical proximity. We consider the network ofshort -range interactions defined asd $${\boldsymbol{\lesssim }}$$ ≲ 1 meter, and thelong -range which includes all interactionsd $${\boldsymbol{\lesssim }}$$ ≲ 10 meters. Since these two networks arise from the same set of underlying behavioral data, we are able to quantitatively measure how the specific definition of the proximity network – short-range versus long-range – impacts the resulting network structure as well as spreading dynamics in epidemic simulations. We find that the short-range network – consistent with the literature – is characterized by densely-connected neighborhoods bridged by weak ties. More surprisingly, however, we show that spreading in the long-range network is quite different, mainly shaped by spurious interactions.