Network reconstruction by stationary distribution data of Markov chains
based on correlation analysis
He, Z.; Xu, R.-J. & Wang, B.-H.
(2014) [pdf]
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
Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions
Toepfer, M.; Kluegl, P.; Hotho, A. & Puppe, F.
, 'Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation' (2011) [pdf]
Markov chain Monte Carlo in practice
Gilks, W. & Spiegelhalter, D.
1996, Chapman & Hall/CRC [pdf]