<sec>
<title>Background</title>
<p>Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities.</p>
</sec><sec>
<title>Methods and Findings</title>
<p>We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections.</p>
</sec><sec>
<title>Conclusions</title>
<p>Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.</p>
</sec>
%0 Journal Article
%1 cattuto2010dynamics
%A Cattuto, Ciro
%A den Broeck, Wouter Van
%A Barrat, Alain
%A Colizza, Vittoria
%A Pinton, Jean-François
%A Vespignani, Alessandro
%D 2010
%I Public Library of Science
%J PLoS ONE
%K rfid sociopatterns
%N 7
%P e11596
%R 10.1371/journal.pone.0011596
%T Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks
%U http://dx.doi.org/10.1371%2Fjournal.pone.0011596
%V 5
%X <sec>
<title>Background</title>
<p>Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities.</p>
</sec><sec>
<title>Methods and Findings</title>
<p>We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections.</p>
</sec><sec>
<title>Conclusions</title>
<p>Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.</p>
</sec>
@article{cattuto2010dynamics,
abstract = {<sec>
<title>Background</title>
<p>Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities.</p>
</sec><sec>
<title>Methods and Findings</title>
<p>We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections.</p>
</sec><sec>
<title>Conclusions</title>
<p>Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.</p>
</sec>},
added-at = {2010-10-20T23:38:15.000+0200},
author = {Cattuto, Ciro and den Broeck, Wouter Van and Barrat, Alain and Colizza, Vittoria and Pinton, Jean-François and Vespignani, Alessandro},
biburl = {https://www.bibsonomy.org/bibtex/20e0e2b183404405b99ffab0b1ca81d5a/stumme},
doi = {10.1371/journal.pone.0011596},
interhash = {ef601366d60939c7d4dd8e2509932bfb},
intrahash = {0e0e2b183404405b99ffab0b1ca81d5a},
journal = {PLoS ONE},
keywords = {rfid sociopatterns},
month = {07},
number = 7,
pages = {e11596},
publisher = {Public Library of Science},
timestamp = {2010-10-20T23:38:15.000+0200},
title = {Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks},
url = {http://dx.doi.org/10.1371%2Fjournal.pone.0011596},
volume = 5,
year = 2010
}