compute.transition.matrix {RandomWalkRestartMH} | R Documentation |
compute.transition.matrix
is a function to compute the transition
matrix of a multiplex heterogeneous network provided as a MultiplexHet
object.
compute.transition.matrix(x,lambda = 0.5, delta=0.5)
x |
A |
lambda |
A numeric value between 0 and 1. It sets the probability of jumping within a network or change to the other network of the heterogeneous system. It is set by default to 0.5. See more details below. |
delta |
A numeric value between 0 and 1. It sets the probability of performing inter-layer versus intra-layer transitions. It is set by default to 0.5. See more details below. |
We clarify the role of the different parameters in this point:
lambda
: For a given node, if a bipartite association exists,
the particle can either jump between networks or stay in the current
graph with a probability given by this parameter. The closer lambda is to
one, the higher is the probability of jumping between networks following
bipartite interactions.
delta
: This parameter sets the probability to change between
layers at the next step. If delta = 0, the particle will always remain
in the same layer after a non-restart iteration. On the other hand, if
delta = 1, the particle will always change between layers, therefore
not following the specific edges of each layer.
A square sparse transition matrix created with the Matrix
package. It is the transition matrix for the Random Walk with Restart on
Multiplex and Heterogeneous networks algorithm.
Alberto Valdeolivas Urbelz alvaldeolivas@gmail.com
create.multiplexHet,
compute.adjacency.matrix
m1 <- igraph::graph(c(1,2,1,3,2,3), directed = FALSE) m2 <- igraph::graph(c(1,3,2,3,3,4,1,4), directed = FALSE) multiObject <- create.multiplex(m1,m2) h1 <- igraph::graph(c("A","C","B","E","E","D","E","C"), directed = FALSE) bipartite_relations <- data.frame(m=c(1,3),h=c("A","E")) multiHetObject <- create.multiplexHet(multiObject, h1,bipartite_relations) compute.transition.matrix(multiHetObject)