multinet: DistanceMeasures – R documentation

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DistanceMeasures

Network analysis measures: distance based

Description

This function is based on the concept of multilayer distance. This concept generalizes single-layer distance to a vector with the distance traveled on each layer (in the "multiplex" case). Therefore, non-dominated path lengths are returned instead of shortest path length, where one path length dominates another if it is not longer on all layers, and shorter on at least one. A non-dominated path length is also known as a Pareto distance. Finding all multilayer distances can be very time-consuming for large networks.

Usage

distance_ml(n, from, to=character(0), method="multiplex")

Arguments

`n`	A multilayer network.
`from`	The actor from which the distance is computed.
`to`	The actor(s) to which the distance is computed. If not specified, all actors are considered.
`method`	This argument can take values "simple", "multiplex", "full". Only "multiplex" is currently implemented.

Value

A data frame with one row for each non-dominated distance, specifying the number of steps in each layer.

References

Magnani, Matteo, and Rossi, Luca (2013). Pareto Distance for Multi-layer Network Analysis. In Social Computing, Behavioral-Cultural Modeling and Prediction (Vol. 7812, pp. 249-256). Springer Berlin Heidelberg.

Examples

net <- ml_aucs()
distance_ml(net,"U54","U3")

multinet

Analysis and Mining of Multilayer Social Networks

v3.3.2

GPL

Authors

Matteo Magnani, Luca Rossi (API design), Obaida Hanteer (mdlpa, flat_ec, flat_nw, some community eval. functions), Davide Vega (API and code design), Mikael Dubik (glouvain). The package uses functions from eclat (www.borgelt.net/eclat.html), for association rule mining, Eigen (eigen.tuxfamily.org) and spectra (https://spectralib.org), for matrix manipulation, Infomap (www.mapequation.org), for the Infomap community detection method, and Howard Hinnant's date and time library (https://github.com/HowardHinnant/date). The code from these libraries has been included in our source package.

Initial release

2021-01-19