Data-driven spreading for the detection of weak ties

International audienceSocial ties of different strengths are assumed to maintain various features in the social network. Following the Granovetterian picture, weak ties are bridging between tightly connected communities, keeping the social structure globally connected, and they are assumed to be critical in disseminating information between far apart sides of the network. Their identification and control can provide effective ways to hinder and postpone outbreaks of spreading phenomena. In this work we propose a new method to infer the strength of social ties by using new data-driven simulation techniques. We return back to the original definition of Granovetter and qualify links by the importance they play during the propagation of information in the social structure. We apply data-driven spreading processes combined with a river-basin algorithmic method (see Fig.1) to identify links, which are the responsible to bring the information to large number of nodes. We investigate the correlations of the new importance measure with other conventional characteristics and identify their best combination through a percolation analysis to sophisticate further the assignment of social tie strengths. Finally we explore the role of the identified high importance links in control of globally spreading processes through data-driven SIR model simulations. These results point out that the size of infected population can be reduced considerably by weakening interactions through ties with high importance but zero overlap compared to strategies based on dyadic communications