Supervised Linear Regression for Graph Learning from Graph Signals

2018/11/05 by Arun Venkitaraman, Venkitaraman, Arun, Hermina Petric Maretić +5
Computer Science · #Advanced Graph Neural Networks #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Graph Theory and Algorithms #Information Theory (cs.IT)

paper · pdf · doi:10.48550/arxiv.1811.01586

openalex publication_date 2018/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
• Learning Graphs From Data: A Signal Representation Perspective

Related

• Predicting Graph Signals using Kernel Regression where the Input Signal is Agnostic to a Graph
• Recursive Prediction of Graph Signals with Incoming Nodes
• Learning Laplacian Matrix in Smooth Graph Signal Representations
• Graph learning under sparsity priors
• Graph Learning from Data under Structural and Laplacian Constraints