A Mutual Selection Model for Weighted Networks

TL;DR

This paper introduces a mutual selection model for weighted networks that reproduces key properties like power-law distributions, clustering, and assortativity, aligning well with empirical observations.

Contribution

It presents a novel mutual selection mechanism that captures the hierarchical and organizational features of weighted networks.

Findings

Reproduces power-law distributions of degree, weight, and strength.

Achieves realistic clustering coefficient and degree assortativity.

Provides insights into network hierarchy and organization.

Abstract

For most networks, the connection between two nodes is the result of their mutual affinity and attachment. In this paper, we propose a mutual selection model to characterize the weighted networks. By introducing a general mechanism of mutual selection, the model can produce power-law distributions of degree, weight and strength, as confirmed in many real networks. Moreover, we also obtained the nontrivial clustering coefficient $C$, degree assortativity coefficient $r$ and degree-strength correlation, depending on a model parameter $m$. These results are supported by present empirical evidences. Studying the degree-dependent average clustering coefficient $C(k)$ and the degree-dependent average nearest neighbors' degree $k_{nn}(k)$ also provide us with a better description of the hierarchies and organizational architecture of weighted networks.