Quantifying Loss of Information in Network-based Dimensionality Reduction Techniques

TL;DR

This paper introduces an algorithmic information theory framework to evaluate how well various network dimensionality reduction techniques preserve information, revealing strengths and weaknesses of methods like motif analysis and spectral sparsification.

Contribution

It provides a novel, rigorous approach to quantify information loss in network reduction methods, enabling better assessment and comparison of these techniques.

Findings

Spectral sparsification is highly sensitive to edge deletion.

Graph spectral methods tend to lose most original information.

Network motif analysis best preserves relative information content.

Abstract

To cope with the complexity of large networks, a number of dimensionality reduction techniques for graphs have been developed. However, the extent to which information is lost or preserved when these techniques are employed has not yet been clear. Here we develop a framework, based on algorithmic information theory, to quantify the extent to which information is preserved when network motif analysis, graph spectra and spectral sparsification methods are applied to over twenty different biological and artificial networks. We find that the spectral sparsification is highly sensitive to high number of edge deletion, leading to significant inconsistencies, and that graph spectral methods are the most irregular, capturing algebraic information in a condensed fashion but largely losing most of the information content of the original networks. However, the approach shows that network motif analysis excels at preserving the relative algorithmic information content of a network, hence validating and generalizing the remarkable fact that despite their inherent combinatorial possibilities, local regularities preserve information to such an extent that essential properties are fully recoverable across different networks to determine their family group to which they belong to (eg genetic vs social network). Our algorithmic information methodology thus provides a rigorous framework enabling a fundamental assessment and comparison between different data dimensionality reduction methods thereby facilitating the identification and evaluation of the capabilities of old and new methods.