IEEE TSIPN · 2020
Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications
IEEE Transactions on Signal and Information Processing over Networks
What determines the sample complexity of network reconstruction?
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.
Research themes
Connections
Recovering structure from limited measurements
Graph recovery estimates electrical network parameters. Charging curve clustering and energy disaggregation recover useful structure from observed behavior and aggregate meter readings.
Cite this paper
Tongxin Li, Lucien Werner, Steven H. Low. Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications. IEEE Transactions on Signal and Information Processing over Networks, 2020. https://doi.org/10.1109/TSIPN.2020.2975368
BibTeX
@article{tongxin-graph-recovery,
title = {{Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications}},
author = {Tongxin Li and Lucien Werner and Steven H. Low},
year = {2020},
journal = {IEEE Transactions on Signal and Information Processing over Networks},
url = {https://doi.org/10.1109/TSIPN.2020.2975368},
doi = {10.1109/TSIPN.2020.2975368}
}
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