Tracking Network Dynamics using Probabilistic StateSpace Models
Abstract
This paper introduces a probabilistic approach for tracking the dynamics of unweighted and directed graphs using statespace models (SSMs). Unlike conventional topology inference methods that assume static graphs and generate pointwise estimates, our method accounts for dynamic changes in the network structure over time. We model the network at each timestep as the state of the SSM, and use observations to update beliefs that quantify the probability of the network being in a particular state. Then, by considering the dynamics of transition and observation models through the update and prediction steps, respectively, the proposed method can incorporate the information of realtime graph signals into the beliefs. These beliefs provide a probability distribution of the network at each timestep, being able to provide both an estimate for the network and the uncertainty it entails. Our approach is evaluated through experiments with synthetic and realworld networks. The results demonstrate that our method effectively estimates network states and accounts for the uncertainty in the data, outperforming traditional techniques such as recursive least squares.
 Publication:

arXiv eprints
 Pub Date:
 September 2024
 DOI:
 10.48550/arXiv.2409.08238
 arXiv:
 arXiv:2409.08238
 Bibcode:
 2024arXiv240908238T
 Keywords:

 Electrical Engineering and Systems Science  Signal Processing
 EPrint:
 Submitted to the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025)