Network reconstruction by stationary distribution data of Markov chains based on correlation analysis

We propose a new method for network reconstruction by the stationary distribution data of Markov chains on this network. Our method has the merits that: the data we need are much few than most method and need not defer to the time order, and we do not need the input data. We define some criterions to measure the efficacy and the simulation results on several networks, including computer-generated networks and real networks, indicate our method works well. The method consist of two procedures, fist, reconstruct degree sequence, second, reconstruct the network(or edges). And we test the efficacy of each procedure.