Uncovering space-independent communities in spatial networks

TL;DR

This paper introduces a new modularity function for detecting communities in spatial networks, allowing the separation of spatial effects to uncover hidden structural similarities, demonstrated on mobile phone data and benchmarks.

Contribution

It proposes a novel community detection method that accounts for spatial constraints, improving the identification of intrinsic network structures beyond spatial influences.

Findings

Effective separation of spatial effects from network structure.

Application to real-world mobile phone data reveals hidden communities.

Method outperforms standard community detection approaches in spatial networks.

Abstract

Many complex systems are organized in the form of a network embedded in space. Important examples include the physical Internet infrastucture, road networks, flight connections, brain functional networks and social networks. The effect of space on network topology has recently come under the spotlight because of the emergence of pervasive technologies based on geo-localization, which constantly fill databases with people's movements and thus reveal their trajectories and spatial behaviour. Extracting patterns and regularities from the resulting massive amount of human mobility data requires the development of appropriate tools for uncovering information in spatially-embedded networks. In contrast with most works that tend to apply standard network metrics to any type of network, we argue in this paper for a careful treatment of the constraints imposed by space on network topology. In particular, we focus on the problem of community detection and propose a modularity function adapted to spatial networks. We show that it is possible to factor out the effect of space in order to reveal more clearly hidden structural similarities between the nodes. Methods are tested on a large mobile phone network and computer-generated benchmarks where the effect of space has been incorporated.