Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: Highlights of the Year

Highlights of the Year

MOSAR results published

The joint analysis of carriage and Close proximity interactions (CPIs) showed that CPI paths linking incident cases to other individuals carrying the same strain (i.e. possible infectors) had fewer intermediaries than predicted by chance (P < 0.001), a feature that simulations showed to be the signature of transmission along CPIs. Additional analyses revealed a higher dissemination risk between patients via healthcare workers than via other patients. In conclusion, S. aureus transmission was consistent with contacts defined by electronically collected CPIs, illustrating their potential as a tool to control hospital-acquired infections and help direct surveillance [19] , [18] .

Time-varying social networks

. We introduce a temporal network model with adjustable community structure and emergent weight-topological correlations via the extension of the activity-driven time-varying network model. the model These model take into account: i) reinforcement processes to model memory-driven interaction dynamics of individuals; ii) focal and cyclic closure to capture patterns responsible for the emerging community structure,; iii) a node removal process. Using this temporal network model we demonstrate the effect of the scalable community structure and social reinforcement on information spreading, which co-evolves with the time-varying interactions [16] .

Stationarity for graph signals

In a series of published works [14] , [40] , [36] , [24] , we formalised the concept of stationarity for graph signals. First, we had to introduce a new definition of graph-shift operator that, in contrast to the current alternatives, is isometric. Then, based on this operator preserving the L2-norm of graph signals, we were able to rigorously characterise the statistical property of wide sense stationarity for graph signals. Stationarity is a central concept in the theory of signal and image processing but was still lacking for graph signals. This contribution should now foster the development of a mathematically sound framework for graph signal processing.


FIT IoT Lab and OneLab received the best demo award at TRIDENTCOM 2015, 10th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities, Vancouver, Canada, June 24–25, 2015.