Bidirectional selection between two classes in complex social networks

TL;DR

This paper investigates how the structure of social networks influences the success rate of bidirectional matching between two classes, highlighting the importance of network degree and topology.

Contribution

It provides a theoretical and quantitative analysis of how different network properties affect bidirectional matching success in complex social networks.

Findings

Higher average degree increases matching success.

Small-world networks outperform scale-free networks in matching rates.

Network structure significantly impacts matching efficiency.

Abstract

The bidirectional selection between two classes widely emerges in various social lives, such as commercial trading and mate choosing. Until now, the discussions on bidirectional selection in structured human society are quite limited. We demonstrated theoretically that the rate of successfully matching is affected greatly by individuals neighborhoods in social networks, regardless of the type of networks. Furthermore, it is found that the high average degree of networks contributes to increasing rates of successful matches. The matching performance in different types of networks has been quantitatively investigated, revealing that the small-world networks reinforces the matching rate more than scale-free networks at given average degree. In addition, our analysis is consistent with the modeling result, which provides the theoretical understanding of underlying mechanisms of matching in complex networks.