Dynamic Sampling Procedure for Decomposable Random Networks
This research studies the problem of node ranking in a random network. Specifically, we consider a Markov chain with several ergodic classes and unknown transition probabilities which can be estimated by sampling. The objective is to select all of the best nodes in each ergodic class. A sampling procedure is proposed to decompose the Markov chain and maximize a weighted probability of correct selection of the best nodes in each ergodic class. Numerical results demonstrate the efficiency of the proposed sampling procedure.
Li, H., Peng, Y., Xu, X., Chen, C.-H., & Heidergott, B. F. (2019). Dynamic sampling procedure for decomposable random networks. 2019 Winter Simulation Conference (WSC), 3752–3763. https://doi.org/10.1109/WSC40007.2019.9004795