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|Community detection is a prominent process on networks and has been extensively studied on static networks the last 25 years. This problem concerns the structural partitioning of networks into classes of nodes that are more densely connected when compared to the rest of the network. However, a plethora of real-world networks are highly dynamic, in the sense that entities (nodes) as well as relations between them (edges) constantly change. As a result, many solutions have also been applied in dynamic/temporal networks under various assumptions concerning the modeling of time as well as the emerging communities. The problem becomes quite harder when the notion of time is introduced, since various unseen problems in the static case arise, like the identity problem. In the last few years, a few surveys have been conducted regarding community detection in time-evolving networks. In this survey, our objective is to give a rather condensed but up-to-date overview, when compared to previous surveys, of the current state-of-the-art regarding community detection in temporal networks. We also extend the previous classification of the algorithmic approaches for the problem by discerning between global and local dynamic community detection. The former aims at identifying the evolution of all communities and the latter aims at identifying the evolution of a partition around a set of seed nodes.|
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