Affiliation:
1. Electrical Engineering and Computer Science, Syracuse University, NY, Syracuse, USA
2. School of Information Studies, Syracuse University, NY, Syracuse, USA
Abstract
It has been observed that real-world social networks often exhibit stratification along economic or other lines, with consequences for class mobility and access to opportunities. With the rise in human interaction data and extensive use of online social networks, the structure of social networks (representing connections between individuals) can be used for measuring stratification. However, although stratification has been studied extensively in the social sciences, there is no single, generally applicable metric for measuring the level of stratification in a network. In this work, we first propose the novel
Stratification Assortativity (StA)
metric, which measures the extent to which a network is stratified into different tiers. Then, we use the
StA
metric to perform an in-depth analysis of the stratification of five co-authorship networks. We examine the evolution of these networks over 50 years and show that these fields demonstrate an increasing level of stratification over time, and, correspondingly, the trajectory of a researcher’s career is increasingly correlated with her entry point into the network.
Funder
Center for Hierarchical Manufacturing, National Science Foundation
Subject
Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology
Cited by
1 articles.
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