@article {42575,
	title = {Online Edge Flow Imputation on Networks},
	journal = {IEEE Signal Processing Letters},
	year = {2022},
	note = {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.},
	publisher = {IEEE},
	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.},
	doi = {10.1109/LSP.2022.3221846},
	url = {https://ieeexplore.ieee.org/document/9947283},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
