Layer aggregation and reducibility of multilayer interconnected networks

TL;DR

This paper introduces an information-theoretic method to reduce the number of layers in multilayer networks, balancing simplification and information preservation, with applications demonstrated on biological interaction data.

Contribution

The paper presents a novel method for multilayer network reduction based on information theory, enabling optimal trade-offs between complexity and accuracy.

Findings

Effective reduction of layers in synthetic benchmarks

Application to protein-genetic interaction data shows variable reducibility

Method preserves key information while simplifying network structure

Abstract

Many complex systems can be represented as networks composed by distinct layers, interacting and depending on each others. For example, in biology, a good description of the full protein-protein interactome requires, for some organisms, up to seven distinct network layers, with thousands of protein-protein interactions each. A fundamental open question is then how much information is really necessary to accurately represent the structure of a multilayer complex system, and if and when some of the layers can indeed be aggregated. Here we introduce a method, based on information theory, to reduce the number of layers in multilayer networks, while minimizing information loss. We validate our approach on a set of synthetic benchmarks, and prove its applicability to an extended data set of protein-genetic interactions, showing cases where a strong reduction is possible and cases where it is not. Using this method we can describe complex systems with an optimal trade--off between accuracy and complexity.