Multiplex PageRank
Arda Halu, Raul J. Mondragon, Pietro Panzarasa, Ginestra Bianconi

TL;DR
This paper introduces Multiplex PageRank, a novel centrality measure for multiplex networks that accounts for interlayer interactions, revealing different node importance rankings compared to single-layer analyses.
Contribution
It proposes a new multiplex centrality measure based on biased random walks, capturing the influence of interlayer interactions on node importance.
Findings
Multiplex PageRank uncovers different node rankings than single-layer methods.
Interlayer interactions significantly affect node importance.
The measure enhances understanding of structural properties in multiplex networks.
Abstract
Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
