Graph Laplacian Learning with Exponential Family Noise

A common challenge in applying graph machine learning methods is that the underlying graph Learning the graph structure underlying a set of smooth signals of a system is often unknown. Although different is a classical problem in GSP. Well-established methods graph inference methods have been proposed optimize a graph representation, usually the graph adjacency for continuous graph signals, inferring the matrix or the graph Laplacian, so that the total variation of graph structure underlying other types of data, given signals will be minimal on the learned graph (Dong such as discrete counts, is under-explored. In et al., 2016; Kalofolias, 2016; Egilmez et al., 2017; Kumar this paper, we generalize a graph signal processing et al., 2020). However, smooth graph signals are rarely (GSP) framework for learning a graph from encountered in the real world and one is often required to smooth graph signals to the exponential family deal with noisy signals.