Universal multilayer network exploration by random walk with restart
authors
keywords
- Applied mathematics
- Biological physics
document type
ARTabstract
The amount and variety of data have been increasing drastically for several years. These data are often represented as networks and explored with approaches arising from network theory. Recent years have witnessed the extension of network exploration approaches to capitalize on 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 networks 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 method and associated Python package that enables Random Walk with Restart on any kind of multilayer network. We evaluate MultiXrank with leave-one-out cross-validation and link prediction, and measure the impact of the addition or removal of network data on prediction performances. Finally, we measure the sensitivity of MultiXrank to input parameters by in-depth exploration of the parameter space.
