  Network embedding approaches are gaining momentum to analyse a large variety
of networks. Indeed, these approaches have demonstrated their efficiency for
tasks such as community detection, node classification, and link prediction.
However, very few network embedding methods have been specifically designed to
handle multiplex networks, i.e. networks composed of different layers sharing
the same set of nodes but having different types of edges. Moreover, to our
knowledge, existing approaches cannot embed multiple nodes from
multiplex-heterogeneous networks, i.e. networks composed of several layers
containing both different types of nodes and edges. In this study, we propose
MultiVERSE, an extension of the VERSE method with Random Walks with Restart on
Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is
a fast and scalable method to learn node embeddings from multiplex and
multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological
and social networks and demonstrate its efficiency. MultiVERSE indeed
outperforms most of the other methods in the tasks of link prediction and
network reconstruction for multiplex network embedding, and is also efficient
in the task of link prediction for multiplex-heterogeneous network embedding.
Finally, we apply MultiVERSE to study rare disease-gene associations using link
prediction and clustering. MultiVERSE is freely available on github at
https://github.com/Lpiol/MultiVERSE.
