Complex networks from lévy noise

Abstract

We construct complex networks from levy noise (LN) using visibility algorithm proposed by Lucas lacasa el al. It is found that as the stability index α of the symmetric LN decreases, the corresponding complex network will transit from exponential network to long-tailed-degree-distribution one, and then to Gaussian one. The associated network for symmetric LN is the high clustering, hierarchy, and 18 community network. The properties of the associated networks for asymmetric LN except the skewness parameter β = −1 are similar with that for symmetric one. The associated network for the asymmetric LN with the skewness parameter β = −1 is always the exponential, high clustering, and hierarchy one with small k-clique communities.