Weighted BA Scale-Free Random Graph Model

Xin Yi Chen

doi:10.4028/www.scientific.net/AMR.433-440.2780

Paper Titles

Selective Facial Expression Recognition Using fastICA
p.2755

The Local Time Step Iterated Timing Analysis Algorithm for Circuit Simulation
p.2762

Prestress Loss Model and Experimental Study of Anchor Cables in Soft Soil Area
p.2769

Querying the Validity of Small-Signal Amplifier Conception
p.2774

Weighted BA Scale-Free Random Graph Model
p.2780

An Attendence System Design Based on Wirless RFID
p.2784

Wind Farm Equivalent Modeling Based on Eigen Analysis
p.2788

A Review of 3D Aircraft Image Recognition Methods
p.2794

Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment
p.2802

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Weighted BA Scale-Free Random Graph Model

Weighted BA Scale-Free Random Graph Model

Abstract:

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of three generic mechanisms: (i) networks expand continuously by the addition of new vertices, (ii) new vertex with different number edges of weighted selected that connected to different vertices in the system, and (iii) new vertices attach preferentially to sites that are already well connected. A model based on these three ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.