  The joint use of node features and network topology to detect communities is
called community detection in attributed networks. Most of the existing work
along this line has been carried out through objective function optimization
and has proposed numerous approaches. However, they tend to focus only on
lower-order details, i.e., capture node features and network topology from node
and edge views, and purely seek a higher degree of optimization to guarantee
the quality of the found communities, which exacerbates unbalanced communities
and free-rider effect. To further clarify and reveal the intrinsic nature of
networks, we conduct triangle-oriented community detection considering node
features and network topology. Specifically, we first introduce a
triangle-based quality metric to preserve higher-order details of node features
and network topology, and then formulate so-called two-level constraints to
encode lower-order details of node features and network topology. Finally, we
develop a local search framework based on optimizing our objective function
consisting of the proposed quality metric and two-level constraints to achieve
both non-overlapping and overlapping community detection in attributed
networks. Extensive experiments demonstrate the effectiveness and efficiency of
our framework and its potential in alleviating unbalanced communities and
free-rider effect.
