Community Detection in Multilayer Networks: Challenges, Opportunities and Applications

TL;DR

This paper reviews the current state, challenges, and future opportunities in community detection within multilayer networks, emphasizing the need for further research and innovation in this complex area.

Contribution

It provides a systematic review of recent advancements, identifies key challenges, and suggests future research directions for community detection in multilayer networks.

Findings

Significant progress has been made across disciplines.

Many questions remain unanswered in multilayer community detection.

Opportunities for innovation and improvement are abundant.

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.