Reduced-Order Estimation of the Uniform Completely Connected Homogeneous Influence Model (UCC-HIM)

Abstract

The influence model (IM) is a discrete-time stochastic automaton that captures spatiotemporal network dynamics. It constitutes a reduced-order representation of networked Markov chains and has found broad stochastic network applications. Parameter estimation from observation data is critical for utilizing IM in real applications. The master Markov chain approach used in the literature incurs significant computational cost. In this letter, we develop an efficient estimation algorithm for a special class of IM, named the uniform completely connected homogeneous influence model (UCC-HIM), through exploiting its special network topology. Specially, we introduce a reduced-order Markov chain representation for the UCC-HIM, analyze its relationship with the master Markov chain, based on which an efficient estimation algorithm is developed. Two simulation studies verify the accuracy and computation reduction of the proposed estimation approach.