Graph

Operating system

Inference

Computer science

Pairwise comparison

Machine learning

World Wide Web

Network science

Theoretical computer science

Artificial intelligence

Graphical model

Complex network

Network topology

IEEE Signal Processing Magazine

Network topology inference is a prominent problem in Network Science. Most graph signal processing (GSP) efforts to date assume that the underlying network is known, and then analyze how the graph's algebraic and spectral characteristics impact the properties of the graph signals of interest. Such an assumption is often untenable beyond applications dealing with e.g., directly observable social and infrastructure networks; and typically adopted graph construction schemes are largely informal, distinctly lacking an element of validation. This tutorial offers an overview of graph learning methods developed to bridge the aforementioned gap, by using information available from graph signals to infer the underlying graph topology. Fairly mature statistical approaches are surveyed first, where correlation analysis takes center stage along with its connections to covariance selection and high-dimensional regression for learning Gaussian graphical models. Recent GSP-based network inference frameworks are also describ...

Connecting the Dots: Identifying Network Structure via Graph Signal Processing