Description Details Author(s) References See Also
This package allows implement descriptive tools and function to handle multiplex networks into the R framework. Multiplex networks are composed by a multiplicity of overlapping layers that capture different types of connection between nodes and account for both relationships within a same layer and relationships between a same node located in different layers of the overall network. Some of the most common descriptors, clustering indices and centrality measures can be calculated starting from a set of nodes and layers given in input by the user. Functions are provided to compute some of the most common descriptors, clustering indices and centrality measures starting from a set of nodes and layers given in input by the user. Definitions and notations used throughout the package make reference to De Domenico et al (2014).
Package: | mplex |
Type: | Package |
Version: | 0.1 |
Date: | 2017-02-26 |
License: | GPL (>=2) |
Functions contained in this package make use of the class multiplex
, created using the function create.multiplex
.
Some functions to easily recover the elements of the multiplex network (the multiplex
-class object) are provided, such as nodes.multiplex
for retrieving the list of nodes, adjacency.multiplex
for the list of all the adjacency matrices of the layers, etc...
Further functions as degree.multiplex
, meanDegree.multiplex
, or densityLayers.multiplex
allow the user to perform some of the basic descriptors for each of the layers composing the multiplex structure of the network.
Functions such as supraAdjacency.multiplex
or aggregatedOverlapping.multiplex
perform basic transformations of the network to structures useful for a first-approach visualization of the multiplex network, e.g. the so-called supra-adjacency matrix, or the aggregated overlapping matrix (see Kivela, M. et al (2014)).
Multiplex-specific functions are provided for computing clustering and centrality measures specifically defined for multiplex networks. Examples are degreeCentrality.multiplex
, supraEigenvectorCentrality.multiplex
or heterEigenvectorCentrality.multiplex
and respectively c1Local.multiplex
, C2Global.multiplex
or globalOverlayClustering.multiplex
.
Emanuele Degani
Maintainer: Emanuele Degani <emanuele.achab@gmail.com>
De Domenico et al (2014). Mathematical formulation of multilayer networks. Phys. Rev. X 3, 041022.
Kivela, M. et al (2014). Multilayer Networks. J. Complex Network. 2(3): 203-271.
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