  Matrix factorization (MF) and Autoencoder (AE) are among the most successful
approaches of unsupervised learning. While MF based models have been
extensively exploited in the graph modeling and link prediction literature, the
AE family has not gained much attention. In this paper we investigate both MF
and AE's application to the link prediction problem in sparse graphs. We show
the connection between AE and MF from the perspective of multiview learning,
and further propose MF+AE: a model training MF and AE jointly with shared
parameters. We apply dropout to training both the MF and AE parts, and show
that it can significantly prevent overfitting by acting as an adaptive
regularization. We conduct experiments on six real world sparse graph datasets,
and show that MF+AE consistently outperforms the competing methods, especially
on datasets that demonstrate strong non-cohesive structures.
