# Modularity of complex networks models

## TL;DR

This paper examines the concept of modularity in complex networks, comparing spatial and non-spatial models, providing theoretical insights, and discussing implications for community detection and model selection.

## Contribution

It offers theoretical results for classical and preferential attachment models and contrasts them with spatial models, enhancing understanding of modularity in different network types.

## Key findings

- Classical random d-regular graphs have low modularity.
- Spatial preferential attachment models naturally produce high modularity.
- Results aid in statistical testing and model selection for network clustering.

## Abstract

Modularity is designed to measure the strength of division of a network into clusters (known also as communities). Networks with high modularity have dense connections between the vertices within clusters but sparse connections between vertices of different clusters. As a result, modularity is often used in optimization methods for detecting community structure in networks, and so it is an important graph parameter from a practical point of view. Unfortunately, many existing non-spatial models of complex networks do not generate graphs with high modularity; on the other hand, spatial models naturally create clusters. We investigate this phenomenon by considering a few examples from both sub-classes. We prove precise theoretical results for the classical model of random d-regular graphs as well as the preferential attachment model, and contrast these results with the ones for the spatial preferential attachment (SPA) model that is a model for complex networks in which vertices are embedded in a metric space, and each vertex has a sphere of influence whose size increases if the vertex gains an in-link, and otherwise decreases with time. The results obtained in this paper can be used for developing statistical tests for models selection and to measure statistical significance of clusters observed in complex networks.