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Semi-Supervised Tracking of Dynamic Processes Over Switching Graphs

Published on Jun 1, 2019
· DOI :10.1109/DSW.2019.8755553
Qin Lu (UMN: University of Minnesota), Vassilis N. Ioannidis2
Estimated H-index: 2
(UMN: University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(UMN: University of Minnesota)
Several network science applications involve nodal processes with dynamics dependent on the underlying graph topology that can possibly jump over discrete states. The connectivity in dynamic brain networks for instance, switches among candidate topologies, each corresponding to a different emotional state. In this context, the present work relies on limited nodal observations to perform semi-supervised tracking of dynamic processes over switching graphs. To this end, leveraging what is termed interacting multi-graph model (IMGM), a scalable online Bayesian approach is developed to track the active graph topology and dynamic nodal process. Numerical tests with synthetic and real datasets demonstrate the merits of the novel approach.
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