AuthorsR. Money, J. Krishnan, B. Beferull-Lozano and E. Isufi
TitleOnline Edge Flow Imputation on Networks
AfilliationMachine Learning
Project(s)Signal and Information Processing for Intelligent Systems
StatusPublished
Publication TypeJournal Article
Year of Publication2022
JournalIEEE Signal Processing Letters
PublisherIEEE
Abstract

A novel online algorithm for missing data imputation for networks with signals defined on the edges is presented in this paper. Leveraging the prior knowledge intrinsic to most real-world networks, we propose a bi-level optimization scheme that includes (i) a sparse line graph identification strategy by solving a group-Lasso-based optimization framework via composite objective mirror descent to exploit the causal dependencies among the signals and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation to exploit the flow conservation. To the best of our knowledge, this is the first SC-based attempt for time-varying signal imputation, whose advantages have been demonstrated through numerical experiments conducted using EPANET models of both synthetic and real water distribution networks.

Notes

This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.

DOI10.1109/LSP.2022.3221846
Citation Key42575

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