  Attributed network embedding (ANE) is to learn low-dimensional vectors so
that not only the network structure but also node attributes can be preserved
in the embedding space. Existing ANE models do not consider the specific
combination between graph structure and attributes. While each node has its
structural characteristics, such as highly-interconnected neighbors along with
their certain patterns of attribute distribution, each node's neighborhood
should be not only depicted by multi-hop nodes, but consider certain clusters
or social circles. To model such information, in this paper, we propose a novel
ANE model, Context Co-occurrence-aware Attributed Network Embedding (CoANE).
The basic idea of CoANE is to model the context attributes that each node's
involved diverse patterns, and apply the convolutional mechanism to encode
positional information by treating each attribute as a channel. The learning of
context co-occurrence can capture the latent social circles of each node. To
better encode structural and semantic knowledge of nodes, we devise a three-way
objective function, consisting of positive graph likelihood, contextual
negative sampling, and attribute reconstruction. We conduct experiments on five
real datasets in the tasks of link prediction, node label classification, and
node clustering. The results exhibit that CoANE can significantly outperform
state-of-the-art ANE models.
