  Network embedding is an effective technique to learn the low-dimensional
representations of nodes in networks. Real-world networks are usually with
multiplex or having multi-view representations from different relations.
Recently, there has been increasing interest in network embedding on multiplex
data. However, most existing multiplex approaches assume that the data is
complete in all views. But in real applications, it is often the case that each
view suffers from the missing of some data and therefore results in partial
multiplex data. In this paper, we present a novel Deep Partial Multiplex
Network Embedding approach to deal with incomplete data. In particular, the
network embeddings are learned by simultaneously minimizing the deep
reconstruction loss with the autoencoder neural network, enforcing the data
consistency across views via common latent subspace learning, and preserving
the data topological structure within the same network through graph Laplacian.
We further prove the orthogonal invariant property of the learned embeddings
and connect our approach with the binary embedding techniques. Experiments on
four multiplex benchmarks demonstrate the superior performance of the proposed
approach over several state-of-the-art methods on node classification, link
prediction and clustering tasks.
