iGraphMatch (version 2.0.5)

graph_match_convex: Frank-Wolfe Graph Matching Methods

Description

Match two given graphs, returns a list of graph matching results, including matching correspondence vector of \(G_2\) with respect to \(G_1\), doubly stochastic matrix and permutation matrix.

Usage

graph_match_convex(
  A,
  B,
  seeds = NULL,
  similarity = NULL,
  start = "bari",
  max_iter = 100,
  tol = 1e-05,
  lap_method = NULL
)
graph_match_indefinite(
  A,
  B,
  seeds = NULL,
  similarity = NULL,
  start = "bari",
  max_iter = 20,
  lap_method = NULL
)
graph_match_PATH(
  A,
  B,
  seeds = NULL,
  similarity = NULL,
  epsilon = 1,
  tol = 1e-05,
  max_iter = 20,
  lap_method = NULL
)

Value

graph_match_indefinite, graph_match_convex and graph_match_PATH

return an object of class "graphMatch" which is a list containing the following components:

corr_A

matching correspondence in \(G_1\)

corr_B

matching correspondence in \(G_2\)

soft

the doubly stochastic matrix from the last iteration with which one can extract more than one matching candidates

iter

number of iterations until convergence or reaches the max_iter

max_iter

Maximum number of replacing matches

lap_method

Choice for solving the LAP

seeds

a vector of logicals indicating if the corresponding vertex is a seed

Arguments

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 \(G_1\) and the second column being the corresponding indices of \(G_2\).

similarity

A matrix. An n-by-n matrix containing vertex similarities.

start

A matrix or a character. Any nns-by-nns matrix or character value like "bari", "rds" or "convex" to initialize the starting matrix.

max_iter

A number. Maximum number of replacing matches.

tol

A number. Tolerance of edge disagreements.

lap_method

Choice for lap method. One of "lapjv", "lapmod", or "clue".

epsilon

A small number

References

Y. Aflalo and A. Bronstein and R. Kimmel (2014), On convex relaxation of graph isomorphism. Proceedings of the National Academy of Sciences, pages 2942-2947.

V. Lyzinski and D. E. Fishkind and M. Fiori and J. T. Vogelstein and C. E. Priebe and G. Sapiro (2016), Graph Matching: Relax at Your Own Risk. IEEE TPAMI, pages 60-73.

V. Lyzinski and D. E. Fishkind and C. E. Priebe (2014), Seeded Graph Matching for Correlated Erdos-Renyi Graphs.J. Mach. Learn. Res., pages 3513-3540.

M. Zaslavskiy, F. Bach and J. Vert (2009), A Path following algorithm for the graph matching problem. IEEE Trans Pattern Anal Mach Intell, pages 2227-2242.

Examples

# \donttest{
cgnp_pair <- sample_correlated_gnp_pair(n = 10, corr =  0.9, p =  0.5)
g1 <- cgnp_pair$graph1
g2 <- cgnp_pair$graph2
# match G_1 & G_2 with no seeds
gm(g1, g2, method = "convex", max_iter = 10)
seeds <- 1:10 <= 3
gm(g1, g2, seeds, method = "convex", max_iter = 10)
# }



# match G_1 & G_2 with some known node pairs as seeds
cgnp_pair <- sample_correlated_gnp_pair(n = 10, corr =  0.3, p =  0.5)
g1 <- cgnp_pair$graph1
g2 <- cgnp_pair$graph2
seeds <- 1:10 <= 3
GM_bari <- gm(g1, g2, seeds, method = "indefinite", start = "bari")
GM_bari
GM_bari[!GM_bari$seeds] # matching correspondence for non-seeds

summary(GM_bari, g1, g2, true_label = 1:10)

# match G_1 & G_2 with some incorrect seeds
hard_seeds <- matrix(c(4,6,5,4),2)
seeds <- rbind(as.matrix(check_seeds(seeds, nv = 10)$seeds),hard_seeds)
GM_badseed <- gm(g1, g2, seeds, method = "indefinite")

GM_badseed[] # get the corresponding permutation matrix
GM_badseed %*% g2 # permute the second graph according to match result: PBP^T
GM_badseed$soft # doubly stochastic matrix from the last step of Frank-Wolfe iterations
GM_badseed$iter # number of iterations
GM_badseed$max_iter # preset maximum number of iterations: 20

# match two multi-layer graphs
gp_list <- replicate(3, sample_correlated_gnp_pair(20, .3, .5), simplify = FALSE)
A <- lapply(gp_list, function(gp)gp[[1]])
B <- lapply(gp_list, function(gp)gp[[2]])

match_multi_layer <- gm(A, B, seeds = 1:10, method = "indefinite", start = "bari", max_iter = 20)
summary(match_multi_layer, A, B)

# match G_1 & G_2 using PATH algorithm
gm(g1, g2, method = "PATH")