Gaussian Networks Generated by Random Walks

Abstract

We propose a random walks based model to generate complex networks. Many authors studied and developed different methods and tools to analyze complex networks by random walk processes. Just to cite a few, random walks have been adopted to perform community detection, exploration tasks and to study temporal networks. Moreover, they have been used also to generate scale-free networks. In this work, we define a random walker that plays the role of "edges-generator". In particular, the random walker generates new connections and uses these ones to visit each node of a network. As result, the proposed model allows to achieve networks provided with a Gaussian degree distribution, and moreover, some features as the clustering coefficient and the assortativity show a critical behavior. Finally, we performed numerical simulations to study the behavior and the properties of the cited model.

12 pages, 6 figures

References in corpus (6)

• Scale-free brain functional networks
• Hyperbolic Geometry of Complex Networks
• Preferential attachment in the growth of social networks: the case of Wikipedia
• A system of mobile agents to model social networks
• The entropic origin of disassortativity in complex networks
• Scale-Free Networks Generated By Random Walkers

Cited by in corpus (2)

• Same Stats, Different Graphs: Exploring the Space of Graphs in Terms of Graph Properties
• Phase-space networks and connectivity of the kagome antiferromagnet