# Layer Communities in Multiplex Networks

## TL;DR

This paper introduces a method for identifying groups of similar layers in multiplex networks by analyzing layer similarities and detecting communities, demonstrated on synthetic and real-world networks.

## Contribution

It presents a novel approach for layer community detection in multiplex networks based on pairwise layer similarity measures.

## Key findings

- Layers with similar functions or geographic locations tend to cluster together.
- The method effectively identifies meaningful layer groups in both synthetic and empirical networks.
- Application examples include airline networks and scientific collaboration networks.

## Abstract

Multiplex networks are a type of multilayer network in which entities are connected to each other via multiple types of connections. We propose a method, based on computing pairwise similarities between layers and then doing community detection, for grouping structurally similar layers in multiplex networks. We illustrate our approach using both synthetic and empirical networks, and we are able to find meaningful groups of layers in both cases. For example, we find that airlines that are based in similar geographic locations tend to be grouped together in an airline multiplex network and that related research areas in physics tend to be grouped together in an multiplex collaboration network.