Several research studies have shown that Complex Networks modeling real-world phenomena are characterized by striking properties: (i) they are organized 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 fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. This survey aims 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 the art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired 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 to orient their future research.lated to the mathematical formalism used to describe them. In Figure 1 are shown four different conceptual Static Edge Weighted Snapshots Temporal Networks Model complexity Temporal impact FIG. 1 Network representations. Moving from static graph to interaction ones the model complexity increases while the grain of the temporal information available decreases (Rossetti, 2015).solutions that gradually introduce the temporal dimension in the network modelling process. At one hand we find the complete atemporal scenario (i.e., a single network snapshot capturing a static glimpse of a dynamic phenomenon): network dynamics can be gradually introduced by adopting labels that weights nodes and edges, thus capturing an aggregate network. Following this approach, weights models the number of occurrence of a given node/edge in a pre-determined observation window: with this little increment in the model complexity, several time-dependent analysis neglected before become possible (i.e., tie strength estimation). Aggregation strategies, however, suffer a severe limitation, they do not capture dynamics. For this reason several works model dynamic phenomena with temporally ordered series of network snapshots. This simple modeling choice allows to efficiently keep track of the perturbations occurring on the network topology. However, while the expressivity of the model is increased, the analytical complexity increases as well. When dealing with network snapshots to perform time-aware mining tasks, two issues need to be addressed: (i) how to keep track of multiple stages of the network life and (ii) how to harmonize the analytical results obtained in a snapshot with the outcome of subsequent ones. Dynamic network partition, as well as aggregation, suffe...

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