How Physical Proximity Shapes Complex Social Networks
Name
How Physical Proximity Shapes Complex Social Networks.pdf
Description
Article
Size
1.51 MB
Format
Adobe PDF
Checksum (MD5)
1f69a66c194bea6657d07e5cae5a2dfe
Author(s)
Stopczynski, Arkadiusz
Pentland, Alex
Lehmann, Sune
Date Issued
December 7, 2018
Publisher
Scientific Reports
Citation
Stopczynski, A., Pentland, A. & Lehmann, S. (2018). How physical proximity shapes complex social networks. Scientific reports, 8(1), 1-10.
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 of short-range interactions defined as d ≲
≲
1 meter, and the long-range which includes all interactions d ≲
≲
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.
MIT Department
MIT Connection Science (Research institute)
Terms of Use
Attribution-NonCommercial-ShareAlike 3.0 United States
Persistent DSpace Link
