Modeling Clustered Scale-free Networks by Applying Various Preferential Attachment Patterns

Year 2018, Issue: 2, 209 - 215, 19.08.2018

Gokhan Kutluana İlker Turker

Abstract

Preferential attachment phenomenon is a key factor
providing scale-free behavior in complex networks. In this study, we introduced
various preferential attachment patterns applied in a growing Barabasi-Albert
network, denoted by a factor α. We first generated networks under constant
preferential attachment levels from 0 to 2, where 1 stands for linear
preferential attachment. Then we performed network simulations under uniformly
distributed random α condition, within the interval [0,2]. Although mean α is 1
for this setup, generated networks displayed greater clustering together with
lower modularity and separation values compared to the setup with α=1. We also
performed similar network generation procedures with various distribution
functions applied for α, each resulting random levels of preferential
attachment. We achieved networks with power-law consistent degree distributions
with γ coefficients between 2 and 3, together with improved clustering
coefficients up to ~0.3. As a result, scale-free network topologies featuring
greater clustering levels compared to pure Barabasi-Albert model are achieved. 

Keywords

Complex network modeling , Preferential attachment , Scale-free networks , Clustering coefficient

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