  In the real world, networks often contain multiple relationships among nodes,
manifested as the heterogeneity of the edges in the networks. We convert the
heterogeneous networks into multiple views by using each view to describe a
specific type of relationship between nodes, so that we can leverage the
collaboration of multiple views to learn the representation of networks with
heterogeneous edges. Given this, we propose a \emph{regularized graph
auto-encoders} (RGAE) model, committed to utilizing abundant information in
multiple views to learn robust network representations. More specifically, RGAE
designs shared and private graph auto-encoders as main components to capture
high-order nonlinear structure information of the networks. Besides, two loss
functions serve as regularization to extract consistent and unique information,
respectively. Concrete experimental results on realistic datasets indicate that
our model outperforms state-of-the-art baselines in practical applications.
