  Most community detection algorithms from the literature work as optimization
tools that minimize a given \textit{fitness function}, while assuming that each
node belongs to a single community. Since there is no hard concept of what a
community is, most proposed fitness functions focus on a particular definition.
As such, these functions do not always lead to partitions that correspond to
those observed in practice. This paper proposes a new flexible fitness function
that allows the identification of communities with distinct characteristics.
Such flexibility was evaluated through the adoption of an immune-inspired
optimization algorithm, named cob-aiNet[C], to identify both disjoint and
overlapping communities in a set of benchmark networks. The results have shown
that the obtained partitions are much closer to the ground-truth than those
obtained by the optimization of the modularity function.
