Complex network construction method with twin peak effect characteristic

Abstract

The invention discloses a complex network construction method with a twin peak effect characteristic. The method mainly comprises the steps of determining an initial network, setting a link edge amount increase mode, determining a newly increased node amount, setting a maximum link edge amount and the like. The degree distribution of a complex network which is obtained by adopting the method presents the twin peak effect characteristic; a twin peak effect derives from a mode that a Logistic equation is used as a node selection strategy to cause node degrees to be increased in a Logistic way, which is different from a mode that a roulette or other mode is commonly used as the node selection strategy to cause the node degrees to be increased in an exponential form; in addition, the movement, the scaling and other control of two peaks of the degree distribution of the complex network are realized by setting the link edge amount increase mode and the maximum link edge amount. The complex network which is obtained by the construction method is different from a classical random network, a small-world network, a scale-free network and a self-similarity network, and the economic and social polarization phenomena in the true life can be better explained.

Description

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AI Technical Summary

Benefits of technology

This technology improves upon traditional networks by providing dynamic changes that allow for greater connections between existing ones while also showing how well they work together under realistic conditions such as changing environmental factors or increasing demand levels. Additionally, it suggests creating models where multiple types of assets have varying degrees of connection within their own structure rather than just one type at once (unimodality). These modifications help predict future movements and improve overall performance across various industries like economics, education, healthcare, etc., making them ideal tools for understanding and analyzing data on these sectors' economy potentials.

Problems solved by technology

This patented technical problem addressed in this patents relates to understanding complexity networks and their behavior under various conditions like temperature or pressure. However, current techniques used for analyzing these systems require detailed knowledge about how they behave over time, making interpretation difficult without comprehensive data sources. Therefore, exploring different aspects related to large-scale structures called complex networks presents challengers due to lack of complete datasets needed for accurate results.

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