A Synthetical Weights' Dynamic Mechanism for Weighted Networks

TL;DR

This paper introduces a dynamic mechanism for weighted networks that models how node strengths, link weights, and new vertices evolve, capturing real-world network behaviors like power-law distributions and clustering.

Contribution

It presents a novel synthetical weights' dynamic mechanism incorporating multiple evolving strategies and analyzes their interactions in network evolution.

Findings

Power-law distributions of strength, degree, and weight.

Nontrivial strength-degree correlation.

Clustering coefficients and assortativeness observed.

Abstract

We propose a synthetical weights' dynamic mechanism for weighted networks which takes into account the influences of strengths of nodes, weights of links and incoming new vertices. Strength/Weight preferential strategies are used in these weights' dynamic mechanisms, which depict the evolving strategies of many real-world networks. We give insight analysis to the synthetical weights' dynamic mechanism and study how individual weights' dynamic strategies interact and cooperate with each other in the networks' evolving process. Power-law distributions of strength, degree and weight, nontrivial strength-degree correlation, clustering coefficients and assortativeness are found in the model with tunable parameters representing each model. Several homogenous functionalities of these independent weights' dynamic strategy are generalized and their synergy are studied.