  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, each pursuing as separate lines of inquiry.
Here we report a series of generic relationships between the quantities
characterizing these two areas by demonstrating that the degree and link weight
distributions 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.
