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Updated: Jun 2, 2026
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Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
Published on: October 29, 2016
Emergence of segregation in evolving social networks
Adam Douglas Henry1, Paweł Prałat, Cun-Quan Zhang
1Division of Public Administration, West Virginia University, Morgantown, WV 26506, USA. adam.henry@mail.wvu.edu
Summary
Social network actors with even a slight aversion to dissimilar connections form segregated networks. Attribute similarity among connected actors stabilizes regardless of network structure or aversion level.
Area of Science:
- Social network analysis
- Computational sociology
- Network science
Background:
- Social networks often exhibit a strong correlation between actor similarity and relationship existence.
- Existing models do not fully capture the emergence of segregation driven by mild preferences.
Purpose of the Study:
- To introduce and analyze a network evolution model incorporating mild aversion to dissimilar connections.
- To demonstrate how segregation emerges in social networks based on attribute similarity.
- To analytically determine the stability of attribute similarity distributions in evolving networks.
Main Methods:
- Development of a network evolution model inspired by Schelling's segregation model.
- Analytical proof of network segregation emergence.
- Mathematical derivation of the stationary distribution of attribute similarity.
Main Results:
- Segregated social networks invariably emerge, irrespective of the degree of aversion to dissimilar connections.
- Attribute similarity among connected actors converges to a stationary distribution.
- This distribution is independent of network topology and the level of aversion bias.
Conclusions:
- Mild aversion to dissimilar connections is sufficient to drive social network segregation.
- The emergent attribute similarity distribution provides a foundational understanding for complex social interaction models.
- This research sheds light on the formation of potentially dysfunctional social network structures.
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