Scientific success from the perspective of the strength of weak ties

Abstract

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.

arXiv comment10 pages, 6 figures

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Cited by in corpus (5)

• Scaling theory of fractal complex networks
• Interplay between tie strength and neighbourhood topology in complex networks: Granovetter's theory and beyond
• Asymmetry of social interactions and its role in link predictability: the case of coauthorship networks
• Beyond traditional box-covering: Determining the fractal dimension of complex networks using a fixed number of boxes of flexible diameter
• Model-based reconstruction of real-world fractal complex networks