Creates a Network with the top results of the Random Walk with restart on a Multiplex and Heterogeneous Network

create.multiplexHetNetwork.topResults is a function to create a network from the top results of the Random Walk with Restart on Multiplex and Heterogeneous networks algorithm (a RWRMH_Results object).

create.multiplexHetNetwork.topResults(RWRMH_Results_Object,
    MultiplexHetObject, bipartite_relations, bipartite_name, k=25)

Arguments

RWRMH_Results_Object	A RWRMH_Results object generated by the function Random.Walk.Restart.MultiplexHet representing the results of the Random Ralk with restart on the multiplex and heterogeneous network described in the following argument.
MultiplexHetObject	A MultiplexHet object generated by the function create.multiplexHet representing a multiplex and heterogeneous network.
bipartite_relations	A data frame containing the relationships (bipartite interactions) between the nodes of the first multiplex network and the nodes of the second multiplex of the heterogeneous system. The data frame should contain two or three columns: the first one with the nodes of the multiplex network; the second one with the nodes of the second network. The third one is not mandatory and it should contain the weights. Every node should be present in their corresponding multiplex network.
bipartite_name	A vector containing the name for the bipartite relations to be integrated as part of the resulting network. It is included as an attribute for all the bipartite edges of the resulting network. It's optional and its default value is "bipartiteRelations".
k	A numeric value between 1 and 200. It is the number of top ranked nodes to be included in the resulting multiplex network.

Value

An igraph object containing the top k ranked multiplex nodes and the top k ranked second network nodes in the Random Walk with Restart on a Multiplex and Heterogeneous network algorithm. We include all the possible types of interactions between pairs of nodes according to the different layers of the multiplex network, the bipartite interactions and the second network type of interactions.

See also

create.multiplexHet, isRWRMH_Results, Random.Walk.Restart.MultiplexHet create.multiplexNetwork.topResults

Author

Alberto Valdeolivas Urbelz alvaldeolivas@gmail.com

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_1 <- create.multiplex(list(m1=m1,m2=m2))
h1 <- igraph::graph(c("A","C","B","E","E","D","E","C"), directed = FALSE)
multiObject_2 <- create.multiplex(list(h1=h1))
bipartite_relations <- data.frame(m=c(1,3),h=c("A","E"))
multiHetObject <- 
    create.multiplexHet(multiObject_1, multiObject_2,bipartite_relations)
#> checking input arguments...
#> Generating bipartite matrix...
#> Expanding bipartite matrix to fit the multiplex network...
MultiHetTranMatrix <- compute.transition.matrix(multiHetObject)
#> Computing adjacency matrix of the first Multiplex network...
#> Computing adjacency matrix of the second Multiplex network...
#> Computing inter-subnetworks transitions...
#> Computing intra-subnetworks transitions...
#> Combining inter e intra layer probabilities into the global 
#>           Transition Matix
Multiplex1_Seeds <- c(1)
Multiplex2_Seeds <- c("E")
RWR_MultiHetResults <- 
    Random.Walk.Restart.MultiplexHet(MultiHetTranMatrix, multiHetObject,
        Multiplex1_Seeds,Multiplex2_Seeds)
create.multiplexHetNetwork.topResults(RWR_MultiHetResults,multiHetObject,
    bipartite_relations)
#> IGRAPH 1bae3de UNW- 9 13 -- 
#> + attr: name (v/c), comp (v/n), weight (e/n), type (e/c)
#> + edges from 1bae3de (vertex names):
#>  [1] 1--2 1--3 2--3 1--3 1--4 2--3 3--4 A--C C--E B--E E--D 1--A 3--E