Complex networks from levy noise

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 alpha 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 beta = -1 are similar with that for symmetric one. The associated network for the asymmetric LN with the skewness parameter beta = -1 is always the exponential, high clustering, and hierarchy one with small k-clique communities.