TY - GEN
T1 - Estimation of Time-Varying Graph Topologies from Graph Signals
AU - Liu, Yuhao
AU - Cui, Chen
AU - Ajirak, Marzieh
AU - Djurić, Petar M.
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In science and engineering, we often deal with signals that are acquired from time-varying systems represented by dynamic graphs. We observe these signals, and the interest is in finding the time-varying topology of the graphs. We propose two Bayesian methods for estimating these topologies without assuming any specific functional relationships among the signals on the graphs. The two methods exploit Gaussian processes, where the first method uses the length scale of the kernel and relies on variational inference for optimization, and the second method is based on derivatives of the functions and Monte Carlo sampling. Both methods estimate the time-varying topologies of the graphs sequentially. We provide numerical tests that show the performance of the methods in two settings.
AB - In science and engineering, we often deal with signals that are acquired from time-varying systems represented by dynamic graphs. We observe these signals, and the interest is in finding the time-varying topology of the graphs. We propose two Bayesian methods for estimating these topologies without assuming any specific functional relationships among the signals on the graphs. The two methods exploit Gaussian processes, where the first method uses the length scale of the kernel and relies on variational inference for optimization, and the second method is based on derivatives of the functions and Monte Carlo sampling. Both methods estimate the time-varying topologies of the graphs sequentially. We provide numerical tests that show the performance of the methods in two settings.
KW - Monte Carlo sampling
KW - estimation of topology
KW - sequential estimation
KW - time-varying graphs
KW - variational inference
UR - https://www.scopus.com/pages/publications/85177546616
U2 - 10.1109/ICASSP49357.2023.10094731
DO - 10.1109/ICASSP49357.2023.10094731
M3 - Conference contribution
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -