Constraints on Meso-Scale Structure in Complex Networks

TL;DR

This paper investigates the detectability of meso-scale structures like core-periphery and nestedness in complex networks, revealing how the configuration model constrains their identification and relating block modularity to stochastic block models.

Contribution

It derives inequalities for when meso-scale structures are detectable under the configuration model and links block modularity with degree-corrected stochastic block models.

Findings

Configuration model restricts detection of certain structures.

Derived inequalities specify detectability conditions.

Connected block modularity with stochastic block models.

Abstract

A key topic in network science is the detection of intermediate or meso-scale structures. Community, core-periphery, disassortative and other partitions allow us to understand the organisation and function of large networks. In this work we study under what conditions certain common meso-scale structures are detectable using the idea of block modularity. We find that the configuration model imposes strong restrictions on core-periphery and related structures in directed networks. We derive inequalities expressing when such structures can be detected under the configuration model. Nestedness is closely related to core-periphery and is similarly restricted to only be detectable under certain conditions. We show that these conditions are a generalisation of the resolution limit to structures other than assortative communities. We show how block modularity is related to the degree corrected Stochastic Block Model and that optimisation of one can be made equivalent to the other in general. Finally, we discuss these issues in inferential versus descriptive approaches to meso-scale structure detection.