Graph Learning from Data under Structural and Laplacian Constraints

Understand

Graphs are fundamental mathematical structures used in various fields to represent data, signals and processes.

• In this paper, we propose a novel framework for learning/estimating graphs from data.
• The proposed framework includes (i) formulation of various graph learning problems, (ii) their probabilistic interpretations and (iii) associated algorithms.
• Specifically, graph learning problems are posed as estimation of graph Laplacian matrices from some observed data under given structural constraints (e.g., graph connectivity and sparsity level).