graph_match_percolation | R Documentation |
Percolation Graph Matching Methods
graph_match_percolation(
A,
B,
seeds,
similarity = NULL,
r = 2,
ExpandWhenStuck = FALSE
)
A |
A matrix, igraph object, or list of either. |
B |
A matrix, igraph object, or list of either. |
seeds |
A vector of integers or logicals, a matrix or a data frame. If
the seed pairs have the same indices in both graphs then seeds can be a
vector. If not, seeds must be a matrix
or a data frame, with the first column being the indices of |
similarity |
A matrix. An |
r |
A number. Threshold of neighboring pair scores. |
ExpandWhenStuck |
A logical. TRUE if expand the seed set when Percolation algorithm stops before matching all the vertices. |
graph_match_percolation
returns an object of class "graphMatch
" which is a
list containing the following components:
matching correspondence in G_1
matching correspondence in G_2
the order of vertices getting matched
a vector of logicals indicating if the corresponding vertex is a seed
L. Yartseva and M. Grossglauser (2013), On the performance of percolation graph matching. COSN, Boston, MA, USA, pages 119–130.
E. Kazemi, S. H. Hassani, and M. Grossglauser (2015), Growing a graph matching from a handful of seeds. Proc. of the VLDB Endowment, 8(10):1010–1021.
# match G_1 & G_2 using percolation graph matching method
cgnp_pair <- sample_correlated_gnp_pair(n = 20, corr = 0.5, p = 0.8)
g1 <- cgnp_pair$graph1
g2 <- cgnp_pair$graph2
seeds <- 1:10 <= 3
GM_perco <- gm(g1, g2, seeds, method = "percolation", r = 2, ExpandWhenStuck = FALSE)
GM_perco
# matching accuracy with the true alignment being the identity
mean(GM_perco$corr_A == GM_perco$corr_B)
GM_perco$match_order
summary(GM_perco, g1, g2, true_label = 1:20)
plot(g1[], g2[], GM_perco)
# expand when stuck
GM_exp <- gm(g1, g2, seeds, method = "percolation", r = 4, ExpandWhenStuck = TRUE)
GM_exp
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