The Strength of Structural Diversity in Online Social Networks
Name
The Strength of Structural Diversity in Online Social Networks.pdf
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3.95 MB
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bcdff9af820397f77dea229f1c87126f
Author(s)
Zhang, Yafei
Wang, Lin
Zhu, Jonathan J. H.
Wang, Xiaofan
Pentland, Alex
Date Issued
May 26, 2021
Publisher
Research
Citation
Zhang, Y., Wang, L., Zhu, J. J., Wang, X., & Pentland, A. S. (2021). The strength of structural diversity in online social networks. Research, 2021.
Abstract
Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social reputation. To capture the structural diversity of an individual, we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into consideration. We show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation, and the inclusion of a coexposure network provides an additional ingredient to achieve that goal. After synthetically controlling several possible confounding factors through matching experiments, structural diversity still plays a nonnegligible role in the prediction of personal online social reputation. Our work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains, such as social influence and collective intelligence studies.
MIT Department
MIT Connection Science (Research institute)
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