Random.Walk.Restart.MultiplexHet: Performs Random Walk with Restart on a Multiplex and...

In RandomWalkRestartMH: Random walk with restart on multiplex and heterogeneous Networks

Description

Random.Walk.Restart.MultiplexHet is a function to perform a Random Walk with Restart on a Multiplex and Heterogeneous network (on a MultiplexHet object). See more details about the algorithm below.

Usage

Random.Walk.Restart.MultiplexHet(...)

## Default S3 method:
Random.Walk.Restart.MultiplexHet(x, MultiplexHet_Object,
  Multiplex_Seed_Nodes, SecondNet_Seed_Nodes, r = 0.7, tau, eta = 0.5, ...)

Arguments

...	Further arguments passed to Random.Walk.Restart.MultiplexHet
x	An object of the Matrix package describing the possible transitions in a multiplex and heterogeneous network.
MultiplexHet_Object	A MultiplexHet object generated by the function create.multiplexHet representing a multiplex and heterogeneous network.
Multiplex_Seed_Nodes	A vector containing the names of the seeds of the multiplex network for the Random Walk algorithm. See more details below.
SecondNet_Seed_Nodes	A vector containing the names of the seeds of the second network for the Random Walk algorithm. See more details below.
r	A numeric value between 0 and 1. It sets the probability of restarting to a seed node after each step. See more details below.
tau	A vector containing the probability of restart on the seeds of the different multiplex layers (layers weights). It must have the same length than the number of layers of the multpiplex network. The sum of its components divided by the number of layers must be 1. See more details below.
eta	A numeric value between 0 and 1. It controls the probability of restarting in each network of the heterogeneous system (Multiplex or second network). See more details below.

Details

Random Walk with Restart simulates an imaginary particle which starts on a seed(s) node(s) and follows randomly the edges of a network. At each step, there is a restart probability, r, meaning that the particle comes back to the seed(s). The extension to multiplex networks allows the particle to explore different monoplex networks (layers). At each step, the particle can also jump to the same node in a different layer. The extension to heterogeneous networks allows the particle to jump between nodes of different nature thanks to bipartite relationships between them. We can combine both, the multiplex and heterogeneous extension, by allowing the particle to jump from a node in every layer of the multiplex network to the other network, and the other way around.

• Multiplex_Seed_Nodes: A vector containing the name of the different seed node(s) of the multiplex network. It's mandatory to provide at least one seed (taking in account both types of seeds) The seed(s) node(s) should belong to any of the layers of the multiplex network. The length of this vector should be smaller than the total number of nodes in the multiplex network.

• SecondNet_Seed_Nodes: A vector containing the name of the different seed node(s) of the second network. It's mandatory to provide at least one seed (taking in account both types of seeds) The seed(s) node(s) should belong to the second network. The length of this vector should be smaller than the total number of nodes in the second network.

• r: A numeric value representing the restart probability on the seeds for the random walker. It must be between 0 and 1. It is set by default to 0.7, which is the most common value in this kind of approaches. It means that, at each step, the walker has a 70% of probability of coming back to one of the seeds.

• tau: A numeric vector containing the probability of restarting in the nodes of the different layers of the multiplex. In the example below, we define the node 1 as the seed node. However, we can find this node in both layers. Therefore, the walker can restart in any of these seed nodes. It is a way to give different relevance (weight) to the different layers.

• eta: A numeric value between 0 and 1 controlling the probability of restarting in the nodes of each network. In the example below, we define the node 1 as a multiplex seed node and "E" as a second network seed node. Therefore, the walker can restart either in the seed 1 or in the seed "E" with different probabilities (it is a way to give more relevance to the different components of the heterogeneous system). If eta < 0.5 the particle will be more likely to restart in one of the multiplex seeds.

Value

A RWRMH_Results object. It contains two sorted rankings: The first one contains the nodes of the multiplex network, except the seeds, along with their score; The second one contains the nodes of the second network, except the seeds, along with their score. In addition, it contains two more fields describing the nodes of different nature used as seeds.

Author(s)

Alberto Valdeolivas Urbelz [e-mail]

See Also

create.multiplexHet, compute.transition.matrix, Random.Walk.Restart.Multiplex isRWRMH_Results

Examples

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)
MultiHetTranMatrix <- compute.transition.matrix(multiHetObject)
Multiplex_Seeds <- c(1)
SecondNet_Seeds <- c("E")
Random.Walk.Restart.MultiplexHet(MultiHetTranMatrix,
    multiHetObject,Multiplex_Seeds,SecondNet_Seeds)

RandomWalkRestartMH documentation built on Nov. 8, 2020, 5:28 p.m.