@article{17139,
  author = {Rohan Money and Joshin Krishnan and Baltasar Beferull-Lozano and Elvin Isufi},
  title = {Online Edge Flow Imputation on Networks},
  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.},
  year = {2022},
  journal = {IEEE Signal Processing Letters},
  publisher = {IEEE},
  url = {https://ieeexplore.ieee.org/document/9947283},
  doi = {10.1109/LSP.2022.3221846},
  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.},
}