Identifying modular flows on multilayer networks reveals highly overlapping organization in social systems

TL;DR

This paper introduces a flow-based method for detecting modular structures in multilayer networks, revealing overlapping communities that are obscured in aggregated network analyses, with applications to scientific collaboration networks.

Contribution

The paper presents a novel flow compression technique specifically designed for multilayer networks, improving community detection accuracy over traditional aggregated approaches.

Findings

Accurately identifies modules in synthetic multilayer networks

Reveals overlapping communities in real-world collaboration networks

Outperforms conventional aggregated methods in capturing network organization

Abstract

Unveiling the community structure of networks is a powerful methodology to comprehend interconnected systems across the social and natural sciences. To identify different types of functional modules in interaction data aggregated in a single network layer, researchers have developed many powerful methods. For example, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow in the systems they represent. However, many networked systems consist of agents or components that exhibit multiple layers of interactions. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here we propose a method based on compression of network flows that can identify modular flows in non-aggregated multilayer networks. Our numerical experiments on synthetic networks show that the method can accurately identify modules that cannot be identified in aggregated networks or by analyzing the layers separately. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks: scientists affiliated to the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method reveals smaller modules with more overlap that better capture the actual organization.