  Deep generative models (DGMs) have achieved remarkable advances.
Semi-supervised variational auto-encoders (SVAE) as a classical DGM offer a
principled framework to effectively generalize from small labelled data to
large unlabelled ones, but it is difficult to incorporate rich unstructured
relationships within the multiple heterogeneous entities. In this paper, to
deal with the problem, we present a semi-supervised co-embedding model for
attributed networks (SCAN) based on the generalized SVAE for heterogeneous
data, which collaboratively learns low-dimensional vector representations of
both nodes and attributes for partially labelled attributed networks
semi-supervisedly. The node and attribute embeddings obtained in a unified
manner by our SCAN can benefit for capturing not only the proximities between
nodes but also the affinities between nodes and attributes. Moreover, our model
also trains a discriminative network to learn the label predictive distribution
of nodes. Experimental results on real-world networks demonstrate that our
model yields excellent performance in a number of applications such as
attribute inference, user profiling and node classification compared to the
state-of-the-art baselines.
