  Nowadays, networks are almost ubiquitous. In the past decade, community
detection received an increasing interest as a way to uncover the structure of
networks by grouping nodes into communities more densely connected internally
than externally. Yet most of the effective methods available do not consider
the potential levels of organisation, or scales, a network may encompass and
are therefore limited. In this paper we present a method compatible with global
and local criteria that enables fast multi-scale community detection. The
method is derived in two algorithms, one for each type of criterion, and
implemented with 6 known criteria. Uncovering communities at various scales is
a computationally expensive task. Therefore this work puts a strong emphasis on
the reduction of computational complexity. Some heuristics are introduced for
speed-up purposes. Experiments demonstrate the efficiency and accuracy of our
method with respect to each algorithm and criterion by testing them against
large generated multi-scale networks. This study also offers a comparison
between criteria and between the global and local approaches.
