# Multiplex PageRank

@article{Halu2013MultiplexP, title={Multiplex PageRank}, author={Arda Halu and Ra{\'u}l J. Mondrag{\'o}n and Pietro Panzarasa and Ginestra Bianconi}, journal={PLoS ONE}, year={2013}, volume={8} }

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…

## 151 Citations

### Novel Multiplex PageRank in Multilayer Networks

- Computer ScienceIEEE Access
- 2018

This paper exploits the concept of populations’ random migration in a multiplex transport network to propose a new Multiplex PageRank centrality measure, where the effects of influence and feedback between networks on the centrality of nodes are directly considered and is applied to an artificial duplex network.

### Multiplex PageRank in Multilayer Networks Considering Shunt

- Computer ScienceSciSec
- 2019

Findings indicate that considering the network with multilayers helps uncover the rankings of nodes, which are different from the rankings in a monotonous network.

### Cross-layer betweenness centrality in multiplex networks with applications

- Computer Science2016 IEEE 32nd International Conference on Data Engineering (ICDE)
- 2016

The proposed CBC measure takes into account the interplay among multiple layers in determining the shortest paths in multiplex networks and is called cross-layer betweenness centrality (CBC).

### Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach

- Computer ScienceSymmetry
- 2019

This paper proposes a centrality measure for biplex networks that extends the adapted PageRank algorithm centrality for spatial networks with data to the PageRank two-layer approach and shows an example where the ability to analyze data referring to a group of people from different aspects and using different sets of independent data are revealed.

### An Eigenvector Centrality for Multiplex Networks with Data

- Computer ScienceSymmetry
- 2019

Networks are useful to describe the structure of many complex systems. Often, understanding these systems implies the analysis of multiple interconnected networks simultaneously, since the system may…

### Centralities of Nodes and Influences of Layers in Large Multiplex Networks

- Computer ScienceJ. Complex Networks
- 2018

The proposed MultiRank takes into account the full multiplex network structure of the data and exploits the dual nature of the network in terms of nodes and layers and assigns more centrality to nodes that receive links from highly influential layers and from already central nodes.

### Weighted Multiplex Networks

- Computer SciencePloS one
- 2014

A theoretical framework based on the entropy of multiplex ensembles is introduced to quantify the information stored in multiplex networks that would remain undetected if the single layers were analyzed in isolation.

### Multilayer networks

- Computer ScienceJ. Complex Networks
- 2014

This chapter shows how interconnected multilayer topology describes such networks more accurately than edge coloring does and introduces the tensor formalism used to construct them.

### Measuring and modelling correlations in multiplex networks

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2015

This work introduces various measures to characterize correlations in the activity of the nodes and in their degree at the different layers and between activities and degrees and shows that real-world networks exhibit indeed nontrivial multiplex correlations.

### Extracting Information from Multiplex Networks

- Computer ScienceChaos
- 2016

The relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks is discussed and the utility of the recently introduced indicator function Θ̃(S) for describing their mesoscale organization and community structure is characterized.

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