  We present the first complete confirmation of Granovetter's theory of social
networks using a massive dataset. For this purpose, we study a scientific
collaboration network, which is considered one of the most important examples
that contradicts the universality of this theory. We achieve this goal by
rejecting the assumption of the symmetry of social ties. Our approach is
grounded in well-established heterogeneous (degree-based) mean-field theory
commonly used to study dynamical processes on complex networks. Granovetter's
theory is based on two hypotheses that assign different roles to interpersonal,
information-carrying connections. The first hypothesis states that strong ties
carrying the majority of interaction events are located mainly within densely
connected groups of people. The second hypothesis maintains that these groups
are connected by sparse weak ties that are of vital importance for the
diffusion of information - individuals who have access to weak ties have an
advantage over those who do not. Given the scientific collaboration network,
with strength of directed ties measured by the asymmetric fraction of joint
publications, we show that scientific success is strongly correlated with the
structure of a scientist's collaboration network. First, among two scientists,
with analogous achievements, the one with weaker ties tends to have the higher
h-index, and second, teams connected by such ties create more valuable
publications.
