dc.contributor.author | Stopczynski, Arkadiusz | |
dc.contributor.author | Pentland, Alex Sandy’ | |
dc.contributor.author | Lehmann, Sune | |
dc.date.accessioned | 2021-10-27T20:11:06Z | |
dc.date.available | 2021-10-27T20:11:06Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/135176 | |
dc.description.abstract | © 2018, The Author(s). 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. | |
dc.language.iso | en | |
dc.publisher | Springer Nature | |
dc.relation.isversionof | 10.1038/S41598-018-36116-6 | |
dc.rights | Creative Commons Attribution 4.0 International license | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Scientific Reports | |
dc.title | How Physical Proximity Shapes Complex Social Networks | |
dc.type | Article | |
dc.contributor.department | Massachusetts Institute of Technology. Media Laboratory | |
dc.relation.journal | Scientific Reports | |
dc.eprint.version | Final published version | |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
eprint.status | http://purl.org/eprint/status/PeerReviewed | |
dc.date.updated | 2019-07-26T17:24:06Z | |
dspace.orderedauthors | Stopczynski, A; Pentland, AS; Lehmann, S | |
dspace.date.submission | 2019-07-26T17:24:08Z | |
mit.journal.volume | 8 | |
mit.journal.issue | 1 | |
mit.metadata.status | Authority Work and Publication Information Needed | |