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 networks with different topologies (e.g., scale-free). 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; moreover we found that some properties of achieved Gaussian networks, 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.