Optimization for Community Detection in Multilayer Networks: A Comprehensive Review and Novel Taxonomy

Community detection is a rapidly growing field, especially for multilayer networks—systems with multiple interaction types. While these networks offer great potential, analyzing them remains complex and underexplored. Recently, researchers have turned to optimization techniques to address these challenges. However, despite diverse approaches, there’s no comprehensive study consolidating these advancements. To bridge this gap, this paper provides a structured review of optimization techniques for community detection in multilayer networks, classifying methods by three criteria: resolution types, optimization types, and resolution methods. This aims to clarify the field and guide future research. This effort seeks to bring clarity to the field, offering a unified perspective on existing methods, while also providing a foundation to inspire and guide future research directions.