SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements
Ying Zhang,
Zhiqiang Zhao,
Zhuo Feng
Abstract:This work introduces a highly-scalable spectral graph densification framework (SGL) for learning resistor networks with linear measurements, such as node voltages and currents. We show that the proposed graph learning approach is equivalent to solving the classical graphical Lasso problems with Laplacian-like precision matrices. We prove that given O(log N ) pairs of voltage and current measurements, it is possible to recover sparse N -node resistor networks that can well preserve the effective resistance dist… Show more
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