WELCOME

We increasingly use digital media and computational devices in our daily activities, and leave behind a sizable amount of digital traces while doing so. The proliferation of mobile devices, and the incorporation of various sensing technologies in these devices, will further add to this growing trail of data. The possibility to mine and analyze these data, and the scale at which this can be done on contemporary computer systems, affords a novel, data-driven approach in the investigation of various aspects of human behavior.

SocioPatterns is an interdisciplinary research collaboration that adopts this data-driven methodology with the aim of uncovering fundamental patterns in social dynamics and coordinated human activity.

To achieve its scientific goals, the SocioPatterns collaboration also contributes to the development of new technologies for collecting relevant data. In particular, the collaboration supports the development of the SocioPatterns sensing platform, which uses wireless wearable sensors to gather longitudinal data on human mobility and  face-to-face proximity in real-world environments.  The SocioPatterns team also works on developing tools and techniques to represent, analyze and visualize the collected data.

FEATURED: INFECTIOUS SOCIOPATTERNS POSTER

Composite image. Left: One of the sixty-nine daily diagrams of contact activity. Right: Thumbnail of the poster with the complete visualization and accompanying text.

Left: One of the sixty-nine daily diagrams of contact activity. Right: Thumbnail of the poster with the complete visualization and accompanying text.

We have created a visualization of sixty-nine days of face-to-face contact activity among more that 30,000 persons based on data collected during the INFECTIOUS: STAY AWAY exhibition in the Science Gallery in Dublin, Ireland. This visualization is published in our gallery as a poster that can be freely downloaded.

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COLLABORATION MEMBERS

SocioPatterns is a collaboration between researchers and developers from the following institutions and companies:

NEWS

New publication and new time-resolved contact data

We have just published a new paper in PLoS ONE, titled “Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors”. This paper describes the properties of the contact patterns between patients, patients and health-care workers (HCWs) and among HCWs in a hospital ward in Lyon, France, from Monday, December 6, 2010 at 1:00 pm to Friday, December 10, 2010 at 2:00 pm. The study included 46 HCWs and 29 patients.

In addition, we release the corresponding time-resolved dataset. The dataset is available here as a tab-separated list of contacts during 20-second intervals of the data collection, and also in gexf format as a supplementary information to the published paper.

A sociometric study on gender homophily

Today we release a new manuscript based on the PhD work of Juliette Stehlé and the behavioral data we collected in a primary school using the SocioPatterns proximity sensors. The manuscript, Gender homophily from spatial behavior in a primary school: a sociometric study , uses high-resolution proximity data among children to investigate gender homophily, its evolution with children age, and its dependence on tie strength.

New manuscript about temporal networks of human contacts

We have released a new preprint dealing with the analysis of temporal networks of human contacts: “Activity clocks: spreading dynamics on temporal networks of human contact“. In this manuscript, we show how spreading processes are an efficient investigation tool of contact networks by focusing on the arrival time distributions of such processes. When computed in terms of “activity clocks” inherent to each node of the network, these distributions are shown to exhibit a very robust behavior. We define hierarchies of null and generative models of time-varying networks and  show that the empirical patterns can be understood in terms of heterogeneous inter-event time distributions coupled with heterogeneous numbers of events per edge. We also show, both empirically and by using a synthetic dataset, that significant deviations from the generic  behavior can be caused by the presence of edge classes with strong activity correlations.

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