  Exploring meaningful structural regularities embedded in networks is a key to
understanding and analyzing the structure and function of a network. The
node-attribute information can help improve such understanding and analysis.
However, most of the existing methods focus on detecting traditional
communities, i.e., groupings of nodes with dense internal connections and
sparse external ones. In this paper, based on the connectivity behavior of
nodes and homogeneity of attributes, we propose a principle model (named GNAN),
which can generate both topology information and attribute information. The new
model can detect not only community structure, but also a range of other types
of structure in networks, such as bipartite structure, core-periphery
structure, and their mixture structure, which are collectively referred to as
generalized structure. The proposed model that combines topological information
and node-attribute information can detect communities more accurately than the
model that only uses topology information. The dependency between attributes
and communities can be automatically learned by our model and thus we can
ignore the attributes that do not contain useful information. The model
parameters are inferred by using the expectation-maximization algorithm. And a
case study is provided to show the ability of our model in the semantic
interpretability of communities. Experiments on both synthetic and real-world
networks show that the new model is competitive with other state-of-the-art
models.
