eigenMapMatMult

Matrix multiplication of the two matrices in input, without copy.

Enables the evaluation of diffusion distances for complex single-layer networks.
Given a network one can define different types of Laplacian (or transition)
matrices corresponding to different continuous-time random walks dynamics on the network.
This package enables the evaluation of Laplacians, stochastic matrices, and the
corresponding diffusion distance matrices. The metric structure induced by the network-driven
process is richer and more robust than the one given by shortest-paths and allows to study
the geometry induced by different types of diffusion-like communication mechanisms taking
place on complex networks.
For more details see: De Domenico, M. (2017) <doi:10.1103/physrevlett.118.168301> and
Bertagnolli, G. and De Domenico, M. (2021) <doi:10.1103/PhysRevE.103.042301>.

Giulia Bertagnolli

diffudist

Diffusion Distance for Complex Networks

Manlio De Domenico

eigenMapMatMult function

<dl><dt>A</dt>
<dd>numeric matrix of dimension \(m \times n\)</dd>
<dt>B</dt>
<dd>numeric matrix of dimension \(n \times l\)</dd></dl>

Arguments

Matrix Multiplication using RcppEigen — eigenMapMatMult

<dl>

<dt>A</dt>
<dd>numeric matrix of dimension \(m \times n\)</dd>


<dt>B</dt>
<dd>numeric matrix of dimension \(n \times l\)</dd>

</dl>

eigenMapMatMult: Matrix Multiplication using RcppEigen

Description

Usage

Value

Arguments