An Analysis of the Matching Hypothesis in Networks
Tao Jia, Robert F. Spivey, Boleslaw Szymanski, Gyorgy Korniss

TL;DR
This paper investigates how network topology influences the matching hypothesis in social networks, revealing that network structure affects attractiveness correlation and matching success in human mate choice models.
Contribution
It extends stochastic models of mate choice to network settings, analyzing how network properties impact matching patterns and attractiveness correlations.
Findings
Attractiveness correlation increases with average degree and decreases with degree diversity.
Sparse networks have lower attractiveness correlation than fully-connected systems.
Matching success probability decreases exponentially with attractiveness and degree.
Abstract
The matching hypothesis in social psychology claims that people are more likely to form a committed relationship with someone equally attractive. Previous works on stochastic models of human mate choice process indicate that patterns supporting the matching hypothesis could occur even when similarity is not the primary consideration in seeking partners. Yet, most if not all of these works concentrate on fully-connected systems. Here we extend the analysis to networks. Our results indicate that the correlation of the couple's attractiveness grows monotonically with the increased average degree and decreased degree diversity of the network. This correlation is lower in sparse networks than in fully-connected systems, because in the former less attractive individuals who find partners are likely to be coupled with ones who are more attractive than them. The chance of failing to be matched…
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