Hidden Markov Modeling over Graphs

TL;DR

This paper introduces a multi-agent filtering algorithm for hidden Markov models on graphs, enabling efficient state estimation and opinion tracking in social networks with proven asymptotic optimality and superior performance over existing methods.

Contribution

It presents a novel multi-agent filtering algorithm for HMMs on graphs with theoretical bounds and empirical validation, advancing social network analysis techniques.

Findings

Algorithm outperforms state-of-the-art social learning methods

Asymptotic bounds established for the filtering accuracy

Experimental results confirm theoretical predictions

Abstract

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference from the optimal centralized Bayesian solution is asymptotically bounded for geometrically ergodic transition models. Experiments illustrate the theoretical findings and in particular, demonstrate the superior performance of the proposed algorithm compared to a state-of-the-art social learning algorithm.