Online Edge Flow Imputation on Networks

Rohan Money, Joshin Krishnan, Baltasar Beferull-Lozano, Elvin Isufi

Research output: Contribution to journalArticleScientificpeer-review

3 Downloads (Pure)


An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via <italic>(i)</italic> a sparse line graph identification strategy based on a group-Lasso and <italic>(ii)</italic> a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation. The advantages of this first SC-based attempt for time-varying signal imputation have been demonstrated through numerical experiments using EPANET models of both synthetic and real water distribution networks.

Original languageEnglish
Pages (from-to)115-119
Number of pages5
JournalIEEE Signal Processing Letters
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Kalman filters
  • Laplace equations
  • Line Graph
  • Missing Flow Imputation
  • Optimization
  • Reactive power
  • Signal processing algorithms
  • Signal reconstruction
  • Simplicial Complex
  • Time series analysis
  • Topological Signal Processing


Dive into the research topics of 'Online Edge Flow Imputation on Networks'. Together they form a unique fingerprint.

Cite this