Graph topology inference benchmarks for machine learning

TL;DR

This paper introduces publicly available benchmarks for evaluating graph inference methods, addressing the challenge of comparing different algorithms' effectiveness across various signal processing and machine learning tasks.

Contribution

It provides standardized benchmarks and comparisons for graph topology inference methods, facilitating fair evaluation and understanding of their strengths and limitations.

Findings

Benchmarks reveal varying performance across inference methods

Comparison highlights strengths and weaknesses of prominent techniques

Facilitates objective evaluation of graph inference algorithms

Abstract

Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As a tool used to express relationships between objects, graphs can be deployed to various ends: I) clustering of vertices, II) semi-supervised classification of vertices, III) supervised classification of graph signals, and IV) denoising of graph signals. However, in many practical cases graphs are not explicitly available and must therefore be inferred from data. Validation is a challenging endeavor that naturally depends on the downstream task for which the graph is learnt. Accordingly, it has often been difficult to compare the efficacy of different algorithms. In this work, we introduce several ease-to-use and publicly released benchmarks specifically designed to reveal the relative merits and limitations of graph inference methods. We also contrast some of the most prominent techniques in the literature.