  The amount and variety of data is increasing drastically for several years.
These data are often represented as networks, which are then explored with
approaches arising from network theory. Recent years have witnessed the
extension of network exploration methods to leverage more complex and richer
network frameworks. Random walks, for instance, have been extended to explore
multilayer networks. However, current random walk approaches are limited in the
combination and heterogeneity of network layers they can handle. New analytical
and numerical random walk methods are needed to cope with the increasing
diversity and complexity of multilayer networks. We propose here MultiXrank, a
Python package that enables Random Walk with Restart (RWR) on any kind of
multilayer network with an optimized implementation. This package is supported
by a universal mathematical formulation of the RWR. We evaluated MultiXrank
with leave-one-out cross-validation and link prediction, and introduced
protocols to measure the impact of the addition or removal of multilayer
network data on prediction performances. We further measured the sensitivity of
MultiXrank to input parameters by in-depth exploration of the parameter space.
Finally, we illustrate the versatility of MultiXrank with different use-cases
of unsupervised node prioritization and supervised classification in the
context of human genetic diseases.
