Spectral entropies as information-theoretic tools for complex network comparison

TL;DR

This paper introduces spectral entropy measures inspired by quantum mechanics to quantify and compare complex networks, enabling model inference, network clustering, and revealing biological community structures.

Contribution

It develops a novel spectral entropy framework for complex networks, facilitating model inference, network comparison, and biological community detection.

Findings

Spectral entropy measures effectively quantify network differences.

Maximum-likelihood inference improves network model fitting.

Hierarchical clustering accurately reveals microbiome community structures.

Abstract

Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Renyi q-entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.