IEEE Transactions on Computational Social Systems (IEEE TCSS) 
Volume 9, Number 3, 2022

Impact Factor: 3.29

Structural Hole Theory in Social Network Analysis: A Review

Abstract:

Social networks now connect billions of people around the world, 
where individuals occupying different positions
often represent different social roles and show different
characteristics in their behaviors. The structural hole theory
demonstrates that users occupying the bridging positions between
different communities possess advantages, since they control the
key information diffusion paths. This type of users, known as
structural hole (SH) spanners, are important when it comes to
assimilating social network structures and user behaviors. In this
article, we review the structural hole theory, where structural
hole spanners take advantage of both information and control
benefits. We investigate the existing algorithms of structural
hole spanner detection, and classify them into information flow-based
algorithms and network centrality-based algorithms. For
practitioners, we further illustrate the applications of structural
hole theory in various practical scenarios, including enterprise
settings, information diffusion in social networks, software development,
mobile applications and machine learning-based social
prediction. Our review provides a comprehensive discussion on
the foundation, detection and practical applications of structural
hole theory. The insights can facilitate researchers and service
developers to better apply the theory and derive value-added tools
with advanced machine learning techniques. To inspire follow-up
research, we identify potential research trends in this area,
especially on the dynamics of networks.


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@article{Lin:TCSS2022,
title = "Structural Hole Theory in Social Network Analysis: A Review",
journal = "IEEE Transactions on Computational Social Systems",
year = "2022",
volume = {9},
number = {3},
pages={724-739},
author = "Zihang Lin, Yuwei Zhang, Qingyuan Gong, Yang Chen, Atte Oksanen, Aaron Yi Ding",
doi={10.1109/TCSS.2021.3070321}
}

Zihang Lin, Yuwei Zhang, Qingyuan Gong, Yang Chen, Atte Oksanen, Aaron Yi Ding, "Structural Hole Theory in Social Network Analysis: A Review", IEEE Transactions on Computational Social Systems, Vol. 9, No. 3, pp. 724-739, 2022. DOI: 10.1109/TCSS.2021.3070321