Efficient Learning for Selecting Important Nodes in Random Network

• Main Authors: Li, Haidong, Xu, Xiaoyun, Peng, Yijie, Chen, Chun-Hung
• Format: text
• Published: Archīum Ateneo 2020
• Subjects: Nonlinear systems; Stochastic processes; Adaptive systems; Uncertainty; Stability analysis; Backstepping; Lyapunov methods; Bayesian learning; dynamic sampling; Markov chain; network; ranking and selection (R&S); Business
• Online Access: https://archium.ateneo.edu/gsb-pubs/67; https://ieeexplore.ieee.org/document/9076790
• Institution: Ateneo De Manila University

In this article, we consider the problem of selecting important nodes in a random network, where the nodes connect to each other randomly with certain transition probabilities. The node importance is characterized by the stationary probabilities of the corresponding nodes in a Markov chain defined o...