@article{cattuto2010dynamics, abstract = { Background

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.

 Methods and Findings

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.

 Conclusions

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.

}, author = {Cattuto, Ciro and den Broeck, Wouter Van and Barrat, Alain and Colizza, Vittoria and Pinton, Jean-François and Vespignani, Alessandro}, doi = {10.1371/journal.pone.0011596}, interhash = {ef601366d60939c7d4dd8e2509932bfb}, intrahash = {0e0e2b183404405b99ffab0b1ca81d5a}, journal = {PLoS ONE}, month = {07}, number = 7, pages = {e11596}, publisher = {Public Library of Science}, 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 } @misc{Barrat2008, abstract = { In this paper we present an experimental framework to gather data on face-to-face social interactions between individuals, with a high spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess contacts with one another by exchanging low-power radio packets. When individuals wear the beacons as a badge, a persistent radio contact between the RFID devices can be used as a proxy for a social interaction between individuals. We present the results of a pilot study recently performed during a conference, and a subsequent preliminary data analysis, that provides an assessment of our method and highlights its versatility and applicability in many areas concerned with human dynamics. }, author = {Barrat, Alain and Cattuto, Ciro and Colizza, Vittoria and Pinton, Jean-Francois and den Broeck, Wouter Van and Vespignani, Alessandro}, interhash = {7868941dd765a5a1f02df5cbfe49c9ef}, intrahash = {f1fb2322336e3f1c7336b0d475311d42}, note = {cite arxiv:0811.4170 }, title = {High resolution dynamical mapping of social interactions with active RFID}, url = {http://arxiv.org/abs/0811.4170}, year = 2008 } @inproceedings{meiss2008ranking, abstract = {We analyze the traffic-weighted Web host graph obtained from a large sample of real Web users over about seven months. A number of interesting structural properties are revealed by this complex dynamic network, some in line with the well-studied boolean link host graph and others pointing to important differences. We find that while search is directly involved in a surprisingly small fraction of user clicks, it leads to a much larger fraction of all sites visited. The temporal traffic patterns display strong regularities, with a large portion of future requests being statistically predictable by past ones. Given the importance of topological measures such as PageRank in modeling user navigation, as well as their role in ranking sites for Web search, we use the traffic data to validate the PageRank random surfing model. The ranking obtained by the actual frequency with which a site is visited by users differs significantly from that approximated by the uniform surfing/teleportation behavior modeled by PageRank, especially for the most important sites. To interpret this finding, we consider each of the fundamental assumptions underlying PageRank and show how each is violated by actual user behavior}, address = {New York, NY, USA}, author = {Meiss, Mark R. and Menczer, Filippo and Fortunato, Santo and Flammini, Alessandro and Vespignani, Alessandro}, booktitle = {WSDM '08: Proceedings of the international conference on Web search and web data mining}, doi = {http://doi.acm.org/10.1145/1341531.1341543}, interhash = {fa73cddce5412036c7aceb46da32b3de}, intrahash = {7515e498f5a5cf00d23c0c8a92099118}, isbn = {978-1-59593-927-9}, location = {Palo Alto, California, USA}, pages = {65--76}, publisher = {ACM}, title = {Ranking web sites with real user traffic}, url = {http://portal.acm.org/citation.cfm?id=1341543}, year = 2008 } @misc{alvarezhamelin-2005, author = {Alvarez-Hamelin, Jose Ignacio and Dall'Asta, Luca and Barrat, Alain and Vespignani, Alessandro}, interhash = {f59a7aa8620977a2ca58e75ae5a03930}, intrahash = {ea1566a1e88a30950615c7d660a9eb6f}, title = {k-core decomposition: a tool for the analysis of large scale Internet graphs}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0511007}, year = 2005 }