Dynamic Sampling Procedure for Decomposable Random Networks

• Main Authors: Li, Haidong, Peng, Yijie, Xu, Xiaoyun, Chen, Chun-Hung, Heidergott, Bernd F
• Format: text
• Published: Archīum Ateneo 2019
• Subjects: Markov processes; Probability; Operations research; Web pages; Social network services; Estimation; Matrix decomposition; Business
• Online Access: https://archium.ateneo.edu/gsb-pubs/65; https://ieeexplore.ieee.org/abstract/document/9004795
• Institution: Ateneo De Manila University

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.