  One of the most important problems in complex networks is how to detect
metadata groups accurately. The main challenge lies in the fact that
traditional structural communities do not always capture the intrinsic features
of metadata groups. Motivated by the observation that metadata groups in PPI
networks tend to consist of an abundance of interacting triad motifs, we define
a 2-club substructure with diameter 2 which possessing triad-rich property to
describe a metadata group. Based on the triad-rich substructure, we design a
DIVision Algorithm using our proposed edge Niche Centrality DIVANC to detect
metadata groups effectively in complex networks. We also extend DIVANC to
detect overlapping metadata groups by proposing a simple 2-hop overlapping
strategy. To verify the effectiveness of triad-rich substructures, we compare
DIVANC with existing algorithms on PPI networks, LFR synthetic networks and
football networks. The experimental results show that DIVANC outperforms most
other algorithms significantly and, in particular, can detect sparse metadata
groups.
