Chaoming Song, Dashun Wang, Albert-Laszlo Barabasi
The increasing availability of large-scale data on human behavior has catalyzed simultaneous advances in network theory, capturing the scaling properties of the interactions between a large number of individuals, and human dynamics, quantifying the temporal characteristics of human activity patterns. These two areas remain disjoint, however, traditionally each pursuing as a separate modeling framework. Here we establish the first formal link between these two areas by showing that the exponents characterizing the degree and link weight distribution in social networks can be expressed in terms of the dynamical exponents characterizing human activity patterns. We test the validity of these theoretical predictions on datasets capturing various facets of human interactions, from mobile calls to tweets. We find evidence of a universal measure, that links networks and human dynamics, but whose value is independent of the means of communication, capturing a fundamental property of human activity.
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http://arxiv.org/abs/1209.1411
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