Community Detection in Multilayer Networks: Challenges, Opportunities and Applications

TL;DR

The paper addresses the challenge of detecting communities in multilayer networks, where multiple interaction types create complex, layered structures. It offers a taxonomy of methods spanning structure-based and embedding-based approaches, including direct, assembly, and flattening paradigms, and surveys their applications across transportation, finance, social, and biological domains. It identifies key challenges—diverse network types, dynamics, scalability, and prior-information limitations—and proposes future directions such as advanced multilayer embeddings and hybrid methods. Overall, the work provides a comprehensive framework and practical guidance for advancing multilayer community detection and its real-world impact.

Abstract

Community detection is a fascinating and rapidly evolving field, but when it comes to analyzing networks with multiple types of interactions, referred to as multilayer networks, there is still a lot of untapped potential. Despite the wide array of methods developed to identify community structures in such networks, this area remains underexplored, leaving plenty of room for innovation. A systematic review of recent advancements is essential to understand where the field stands and where it is headed. While significant strides have been made across various disciplines, many questions remain unanswered, and new opportunities are waiting to be uncovered. In this paper, we explore the different types of multilayer networks, community detection techniques, and how they are applied in real world scenarios. We also dive into the key challenges researchers face and suggest potential directions for future work, aiming to refine community detection techniques and boost their effectiveness in multilayer networks.

Figures (2)

• Figure 1: Example of multilayer networks.
• Figure 2: Example of community detection in multilayer networks.