%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 }