Two-way selection on complex weighted networks

Original Article
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Abstract

The phenomena of two-way selection among people are universal. In the paper, we depict the model of two-way selection in complex weighted networks and give its mathematical theory tools for uncovering the dynamics of human behaviors. We present two definitions: direct matching and indirect matching, which exist in social life. Based on priority, we give two different methods that how an individual to find a match object. In the former case, we consider an individual’s neighbors as his selections. In the latter case, based on the Markov process we develop a method that how an individual to find a match object through its neighbors in complex weighted networks. Our framework is of both fundamental and practical interest, as it provides a novel understanding of the interplay between weighted networks and two-way selection.

Keywords

Two-way selection Complex weighted networks Preference Matching Modeling 

Notes

Acknowledgements

We would like to thank Z.M. Gao for many interesting and inspiring discussions. We wish to express our sincere appreciation to all those who made suggestions for improvements to this paper. Particularly, we thank Y.P. Liang with expertise in technical English editing for the English of this manuscript being improved.

Funding

This work was supported by Innovation Foundations of Education for Graduate Students of Shanxi Province (Grant No. 2016BY061) and the National Natural Science Foundation of China (Grant Nos. 61402319, 61503271). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author contributions

YY conceived and designed the research, analyzed and described the analytic model, wrote the paper, prepared figures, performed the computation work. GX conceived and designed the research, analyzed the data, reviewed drafts of the paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.College of Information EngineeringTaiyuan University of TechnologyTaiyuanPeople’s Republic of China

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