Abstract
Network-science related applications frequently deal with inference of spatio-temporal processes. Such inference tasks can be aided by a graph whose topology contributes to the underlying spatio-temporal dependencies. Contemporary approaches extrapolate dynamic processes relying on a fixed dynamical model, that is not adaptive to changes in the dynamics. Alleviating this limitation, the present work adopts a candidate set of graph-adaptive dynamical models with one active at any given time. Given partially observed nodal samples, a scalable Bayesian tracker is leveraged to infer the graph processes and learn the active dynamical model simultaneously in a data-driven fashion. The resulting algorithm is termed graph-adaptive interacting multiple dynamical models (Grad-IMDM). Numerical tests with synthetic and real data corroborate that the proposed Grad-IMDM is capable of tracking the graph processes and adapting to the dynamical model that best fits the data.
Original language | English (US) |
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Title of host publication | Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 1783-1787 |
Number of pages | 5 |
ISBN (Electronic) | 9781728143002 |
DOIs | |
State | Published - Nov 2019 |
Event | 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States Duration: Nov 3 2019 → Nov 6 2019 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2019-November |
ISSN (Print) | 1058-6393 |
Conference
Conference | 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/3/19 → 11/6/19 |
Bibliographical note
Funding Information:This work was supported in part by NSF grants 1508993, 1711471, and 1901134.
Publisher Copyright:
© 2019 IEEE.
Keywords
- Bayesian tracker
- Spatiotemporal process
- multiple dynamical models