  Networks are ubiquitous structure that describes complex relationships
between different entities in the real world. As a critical component of
prediction task over nodes in networks, learning the feature representation of
nodes has become one of the most active areas recently. Network Embedding,
aiming to learn non-linear and low-dimensional feature representation based on
network topology, has been proved to be helpful on tasks of network analysis,
especially node classification. For many real-world systems, multiple types of
relations are naturally represented by multiple networks. However, existing
network embedding methods mainly focus on single network embedding and neglect
the information shared among different networks. In this paper, we propose a
novel multiple network embedding method based on semisupervised autoencoder,
named DeepMNE, which captures complex topological structures of multi-networks
and takes the correlation among multi-networks into account. We evaluate
DeepMNE on the task of node classification with two real-world datasets. The
experimental results demonstrate the superior performance of our method over
four state-of-the-art algorithms.
