Community Discovery in Dynamic Networks: a Survey

TL;DR

This survey reviews methods for detecting evolving communities in dynamic networks, highlighting their features, challenges, and classification to guide future research and practical applications.

Contribution

It provides a comprehensive taxonomy of existing approaches to dynamic community discovery, aiding researchers in selecting suitable methods based on network dynamics and analytical needs.

Findings

Classifies approaches based on rationale and instantiation

Identifies key challenges in dynamic community detection

Guides future research directions in the field

Abstract

Networks built to model real world phenomena are characeterised by some properties that have attracted the attention of the scientific community: (i) they are organised according to community structure and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and challenging problem started capturing researcher interest recently: the identification of evolving communities. To model the evolution of a system, dynamic networks can be used: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches. As a "user manual", this work organizes state of art methodologies into a taxonomy, based on their rationale, and their specific instanciation. Given a desired definition of network dynamics, community characteristics and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers to choose in which direction should future research be oriented.