SGL: Spectral Graph Learning from Measurements
Hosted in Virtual Platform
Digital Design, Timing and Simulation
DescriptionThis work introduces a highly-scalable spectral densification framework for learning resistor networks with linear measurements, such as node voltages and currents. We show that given O(log N) pairs of voltage and current measurements, it is possible to recover ultra-sparse N-node resistor networks that can well preserve the effective resistance distances on the graph. In addition, the learned graphs also preserve the structural (spectral) properties of the original graph, which can potentially be leveraged in many circuit design and optimization tasks. Compared with prior state-of-the-art graph learning approaches, our approach is more scalable for learning large networks without sacrificing solution quality.