2012/05/31 by Saeed Mehraban, S. Mehraban, Mohammad Reza Ejtehadi +3 · 2 citations
Mathematics · Physics and Astronomy · #Artificial intelligence #Average path length #Cluster analysis #Clustering coefficient #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer science #Degree distribution #Dynamic network analysis #Exponent #FOS: Physical sciences #Graph #Mathematics #Opinion Dynamics and Social Influence #Physics #Physics and Society (physics.soc-ph) #Population #Random graph #Random walk #Scaling #Shortest path problem #Statistical Mechanics (cond-mat.stat-mech) #Statistical physics #Statistics #Theoretical and Computational Physics #Theoretical computer science #Transition point #cond-mat.stat-mech #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1205.7069
published in arXiv (Cornell University) (Cornell University)
arxiv created 2012/05/31 · openalex publication_date 2012/05/31 · arxiv updated 2012/06/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We introduce a self-organized model of graph evolution associated with preferential network random walkers. The idea is developed by using two different types of walkers, the interactions of which lead to a dynamic graph. The walkers of the first type cause an enhancement in link attachments, while the second types have a destructive behavior. The statistical properties of the resulting network, including weight distributions, clustering, spectral densities and average path length are evaluated. As the ratio of the population of two types is balanced, the network faces a phase transition. We show that in the transition point, the graph behaves as a scale-free network, with a scaling exponent of ∼ -1.7.