  Community structure is largely regarded as an intrinsic property of complex
real-world networks. However, recent studies reveal that networks comprise even
more sophisticated modules than classical cohesive communities. More precisely,
real-world networks can also be naturally partitioned according to common
patterns of connections between the nodes. Recently, a propagation based
algorithm has been proposed for the detection of arbitrary network modules. We
here advance the latter with a more adequate community modeling based on
network clustering. The resulting algorithm is evaluated on various synthetic
benchmark networks and random graphs. It is shown to be comparable to current
state-of-the-art algorithms, however, in contrast to other approaches, it does
not require some prior knowledge of the true community structure. To
demonstrate its generality, we further employ the proposed algorithm for
community detection in different unipartite and bipartite real-world networks,
for generalized community detection and also predictive data clustering.
