Structured Graph Learning Via Laplacian Spectral Constraints

Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. It is well known that structured graph learning from observedsamples isanNP-hard combinatorial problem. In this paper, we first show that for a set of important graph families it is possible toconvertthestructural constraints ofstructure intoeigenvalueconstraints ofthe graph Laplacianmatrix.