Community Discovery in Dynamic Networks: a Survey
Giulio Rossetti, R\'emy Cazabet

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…
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