  Many methods have been proposed to detect communities, not only in plain, but
also in attributed, directed or even dynamic complex networks. From the
modeling point of view, to be of some utility, the community structure must be
characterized relatively to the properties of the studied system. However, most
of the existing works focus on the detection of communities, and only very few
try to tackle this interpretation problem. Moreover, the existing approaches
are limited either by the type of data they handle, or by the nature of the
results they output. In this work, we see the interpretation of communities as
a problem independent from the detection process, consisting in identifying the
most characteristic features of communities. We give a formal definition of
this problem and propose a method to solve it. To this aim, we first define a
sequence-based representation of networks, combining temporal information,
community structure, topological measures, and nodal attributes. We then
describe how to identify the most emerging sequential patterns of this dataset,
and use them to characterize the communities. We study the performance of our
method on artificially generated dynamic attributed networks. We also
empirically validate our framework on real-world systems: a DBLP network of
scientific collaborations, and a LastFM network of social and musical
interactions.
