graphMatch-class | R Documentation |
An S4 class for the results of a graph matching function
graphMatch(corr, nnodes, call = NULL, detail = list())
as.graphMatch(from)
corr |
data.frame indicating the correspondence between two graphs |
nnodes |
dimensions of the original two graphs |
call |
The call to the graph matching function |
detail |
List with other more detailed information |
from |
object to convert to graphMatch object |
graphMatch objects are returned by any of the graph matching methods implemented in the iGraphMatch package. These objects are primarily to represent the found correspondence between the two vertex sets. This is represented by a data.frame with two columns indicating the aligned vertex-pairs across the two graphs.
graphMatch object
corr
data.frame indicating the correspondence between two graphs
nnodes
of the original two graphs
call
The call to the graph matching function
graphMatch_methods, graphMatch_summary, graphMatch_operators, graphMatch_plot
# sample a pair of correlated random graphs from G(n,p)
set.seed(123)
cgnp_pair <- sample_correlated_gnp_pair(n = 10, corr = 0.3, p = 0.5)
g1 <- cgnp_pair$graph1
g2 <- cgnp_pair$graph2
# match g1 & g2 using percolation algorithm with some known node pairs as seeds
match <- gm(A = g1, B = g2, seeds = 1:3, method = 'indefinite')
# graphMatch object
match
match$corr_A # matching correspondence in the first graph
match$corr_B # matching correspondence in the second graph
match$seeds # vector of logicals indicating seeded nodes
as.data.frame(match)
match[]
dim(match)
length(match)
# matching details unique to the FW methodology with indefinite relaxation
match$iter # number of iterations
match$soft # doubly stochastic matrix from the last iteration, can be used to extract soft matching
match$lap_method # method for solving lap
# create a graphMatch object from a data.frame or matrix
as.graphMatch(data.frame(1:5, 1:5))
as.graphMatch(1:5)
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