  Complex real-world networks commonly reveal characteristic groups of nodes
like communities and modules. These are of value in various applications,
especially in the case of large social and information networks. However, while
numerous community detection techniques have been presented in the literature,
approaches for other groups of nodes are relatively rare and often limited in
some way. We present a simple propagation-based algorithm for general group
detection that requires no a priori knowledge and has near ideal complexity.
The main novelty here is that different types of groups are revealed through an
adequate hierarchical group refinement procedure. The proposed algorithm is
validated on various synthetic and real-world networks, and rigorously compared
against twelve other state-of-the-art approaches on group detection, hierarchy
discovery and link prediction tasks. The algorithm is comparable to the state
of the art in community detection, while superior in general group detection
and link prediction. Based on the comparison, we also dis- cuss some prominent
directions for future work on group detection in complex networks.
