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In most social and information systems the activity of agents generatesrapidly evolving time-varying networks. The temporal variation in networks'connectivity patterns and the ongoing dynamic processes are usually coupled inways that still challenge our mathematical or computational modelling. Here weanalyse a mobile call dataset and find a simple statistical law thatcharacterize the temporal evolution of users' egocentric networks. We encodethis observation in a reinforcement process defining a time-varying networkmodel that exhibits the emergence of strong and weak ties. We study the effectof time-varying and heterogeneous interactions on the classic rumour spreadingmodel in both synthetic, and real-world networks. We observe that strong tiesseverely inhibit information diffusion by confining the spreading process amongagents with recurrent communication patterns. This provides thecounterintuitive evidence that strong ties may have a negative role in thespreading of information across networks.

Time varying networks and the weakness of strong ties