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.

@misc{he2014network, author = {He, Zhe and Xu, Rui-Jie and Wang, Bing-Hong}, title = {Network reconstruction by stationary distribution data of Markov chains
based on correlation analysis}, year = {2014}, note = {cite arxiv:1410.4120Comment: 4 pages, 3 figures}, url = {http://arxiv.org/abs/1410.4120}, keywords = {chain, markov, network, reconstruction, stationary}, abstract = {We propose a new method for network reconstruction by the stationarydistribution data of Markov chains on this network. Our method has the meritsthat: the data we need are much few than most method and need not defer to thetime order, and we do not need the input data. We define some criterions tomeasure the efficacy and the simulation results on several networks, includingcomputer-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 eachprocedure.} }

%0 = misc %A = He, Zhe and Xu, Rui-Jie and Wang, Bing-Hong %B = } %C = %D = 2014 %I = %T = Network reconstruction by stationary distribution data of Markov chains
based on correlation analysis} %U = http://arxiv.org/abs/1410.4120