  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.
