# Supervised Linear Regression for Graph Learning from Graph Signals

**Authors:** Arun Venkitaraman, Hermina Petric Maretic, Saikat Chatterjee, Pascal Frossard  
**arXiv:** [1811.01586](https://arxiv.org/abs/1811.01586)  
**Date:** 2018-11-05  
**Summary page:** https://www.summarizepaper.com/fr/paper/1811.01586v1/

## Abstract

We propose a supervised learning approach for predicting an underlying graph from a set of graph signals. Our approach is based on linear regression. In the linear regression model, we predict edge-weights of a graph as the output, given a set of signal values on nodes of the graph as the input. We solve for the optimal regression coefficients using a relevant optimization problem that is convex and uses a graph-Laplacian based regularization. The regularization helps to promote a specific graph spectral profile of the graph signals. Simulation experiments demonstrate that our approach predicts well even in presence of outliers in input data.