Large Scale Graph Learning from Smooth Signals

TL;DR

This paper introduces a scalable graph learning algorithm that approximates the quality of exact models with significantly reduced computational cost, enabling large-scale data analysis using smooth signals.

Contribution

It presents a novel method that reduces graph learning complexity from quadratic to near-linear time using approximate nearest neighbors and automatic parameter selection.

Findings

Achieves $ ext{O}(n ext{log}(n))$ complexity for graph learning.

Maintains quality close to exact graph learning models.

Requires only the desired edge density as input.

Abstract

Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typically they are constructed either by connecting nearest samples, or by learning them from data, solving an optimization problem. While graph learning does achieve a better quality, it also comes with a higher computational cost. In particular, the current state-of-the-art model cost is $\mathcal{O}(n^2)$ for $n$ samples. In this paper, we show how to scale it, obtaining an approximation with leading cost of $\mathcal{O}(n\log(n))$, with quality that approaches the exact graph learning model. Our algorithm uses known approximate nearest neighbor techniques to reduce the number of variables, and automatically selects the correct parameters of the model, requiring a single intuitive input: the desired edge density.