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.