Systems biology beyond degree, hubs and scale-free networks: the case for multiple metrics in complex networks

TL;DR

This paper advocates for using multiple network metrics in the analysis of complex biological systems to better identify informative entities and improve network comparisons, moving beyond traditional single-metric approaches.

Contribution

It introduces a holistic approach employing multiple metrics and recent techniques for more effective network analysis and comparison in complex systems.

Findings

Multiple metrics improve network entity identification

Holistic comparison enhances understanding of growth models

Traditional metrics are insufficient alone for complex networks

Abstract

Modeling and topological analysis of networks in biological and other complex systems, must venture beyond the limited consideration of very few network metrics like degree, betweenness or assortativity. A proper identification of informative and redundant entities from many different metrics, using recently demonstrated techniques, is essential. A holistic comparison of networks and growth models is best achieved only with the use of such methods.