3 research outputs found
Gaussian Networks Generated by Random Walks
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.Comment: 12 pages, 6 figure