Network reconstruction by stationary distribution data of Markov chains

based on correlation analysis

based on correlation analysis

We propose a new method for network reconstruction by the stationary

stribution data of Markov chains on this network. Our method has the merits

at: the data we need are much few than most method and need not defer to the

me order, and we do not need the input data. We define some criterions to

asure the efficacy and the simulation results on several networks, including

mputer-generated networks and real networks, indicate our method works well.

e method consist of two procedures, fist, reconstruct degree sequence,

cond, reconstruct the network(or edges). And we test the efficacy of each

ocedure.

stribution data of Markov chains on this network. Our method has the merits

at: the data we need are much few than most method and need not defer to the

me order, and we do not need the input data. We define some criterions to

asure the efficacy and the simulation results on several networks, including

mputer-generated networks and real networks, indicate our method works well.

e method consist of two procedures, fist, reconstruct degree sequence,

cond, reconstruct the network(or edges). And we test the efficacy of each

ocedure.