Description Usage Arguments Details Value Author(s) References Examples
This function computes the Ipsen-Mikhailov distance between two multiplex networks.
1 |
x |
A list object, see Description for more details. |
y |
A list object, see Description for more details. |
d |
|
ga |
|
components |
|
... |
Additional arguments to be passed to the downstream functions. Normally the argument passed through ... are processed by the functions which compute the distance. Not all parameters are used by all functions. |
mpnetdist
is a high level function which provides an interface to
the Ipsen-Mikhailov distance for multiplex-network.
The network can have 1 or more layers with the same number of nodes
per layer.
Multiplex network can be represented using a list object, with two
element I (interlayer adjacency matrix) and L (intralayer adjacency
matrix).
Given a network with l layers and n nodes per layer the object I of
the list should be a matrix of dimension l \times l with no self-loops
hence the diagonal should be a vector of zeros.
The second element of the list (L) is a 3d array. The array should
contain the adjacency matrix for each layer and should be a n \times
n \times l array.
Parallel computation is provided automatically through the
parallel package included by default from R 2.15.
The computation can be automatically parallelized on a multi-cpu
computer using the parameter n.cores
.
We suggest not to change ga
and use the automatic
computation of the parameter based on the number of nodes in the
network.
The Ipsen-Mikhailov distance value between the input network.
M. Filosi
A. Sole-Ribalta, M. De Domenico, N. E. Kouvaris, A. Diaz-Guilera,
S. gomez, A. Arenas Spectral Properties of the Laplacian of
multiplex networks, 2013, arXiv:1307.2090v1
G. Jurman, R. Visintainer, M. Filosi, S. Riccadonna, C. Furlanello
The HIM glocal metric and kernel for network comparison and classification arXiv 2013, arXiv:1201.2931v3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Create toy dataset
a <- matrix(rnorm(1000),ncol=100)
b <- matrix(rnorm(1000),ncol=100)
cc <- matrix(rnorm(1000),ncol=100)
## Compute adjacency matrix!
## For different methods check the mat2adj help page!
aadj <- mat2adj(a, infer.method="MINE", n.cores=1)
badj <- mat2adj(b, infer.method="MINE", n.cores=1)
cadj <- mat2adj(cc, infer.method="MINE", n.cores=1)
myarr <- array(data=NA, c(100,100,3))
myarr[,,1] <- aadj
myarr[,,2] <- badj
myarr[,,3] <- cadj
iadj <- matrix(c(0,1,0,1,0,1,0,1,0), ncol=3, byrow=TRUE)
net1 <- list(I=iadj, L=myarr)
net2 <- net1
dd <- mpnetdist(net1, net2, n.cores=1)
## IM
## 0
|
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