CDC · 2019

Learning Graph Parameters from Linear Measurements: Fundamental Trade-offs and Application to Electric Grids

Tongxin Li, Lucien Werner, Steven H. Low

IEEE Conference on Decision and Control

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How many measurements are needed to recover a network?

Information theoretic bounds relate measurement requirements to graph complexity and recovery error. A recovery algorithm estimates topology and parameters from linear measurements, with demonstrations on graph families and electrical grid test cases.

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Cite this paper

Tongxin Li, Lucien Werner, Steven H. Low. Learning Graph Parameters from Linear Measurements: Fundamental Trade-offs and Application to Electric Grids. IEEE Conference on Decision and Control, 2019. https://doi.org/10.1109/CDC40024.2019.9029949

BibTeX
@inproceedings{tongxin-graph-parameters,
  title = {{Learning Graph Parameters from Linear Measurements: Fundamental Trade-offs and Application to Electric Grids}},
  author = {Tongxin Li and Lucien Werner and Steven H. Low},
  year = {2019},
  booktitle = {IEEE Conference on Decision and Control},
  url = {https://doi.org/10.1109/CDC40024.2019.9029949},
  doi = {10.1109/CDC40024.2019.9029949}
}
2020IEEE TSIPN

Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications

Tongxin Li, Lucien Werner, Steven H. Low

Bounds for noisy and noiseless graph recovery connect sparsity, graph distributions, and linear measurements. A three stage recovery scheme and practical algorithm are studied on canonical graphs and electrical grid admittance matrices.

Information Energy Paper
What determines the sample complexity of network reconstruction? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization