  Graph neural networks have gained prominence due to their excellent
performance in many classification and prediction tasks. In particular, they
are used for node classification and link prediction which have a wide range of
applications in social networks, biomedical data sets, and financial
transaction graphs. Most of the existing work focuses primarily on the monoplex
setting where we have access to a network with only a single type of connection
between entities. However, in the multiplex setting, where there are multiple
types of connections, or \emph{layers}, between entities, performance on tasks
such as link prediction has been shown to be stronger when information from
other connection types is taken into account. We propose an algorithm for
scalable learning on multiplex networks with a large number of layers. The
efficiency of our method is enabled by an online learning algorithm that learns
how to sample relevant neighboring layers so that only the layers with relevant
information are aggregated during training. This sampling differs from prior
work, such as MNE, which aggregates information across \emph{all} layers and
consequently leads to computational intractability on large networks. Our
approach also improves on the recent layer sampling method of \textsc{DeePlex}
in that the unsampled layers do not need to be trained, enabling further
increases in efficiency.We present experimental results on both synthetic and
real-world scenarios that demonstrate the practical effectiveness of our
proposed approach.
