Deep Partial Multiplex Network Embedding

Qifan Wang, Anirudh Ravula, Ruining He, Jingang Wang

Abstract:

Network embedding is an effective technique to learn the
low-dimensional representations of nodes in networks. Realworld 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 stateof-the-art methods on node classification, link prediction and
clustering tasks.