gcor
finds the product-moment correlation between the adjacency matrices of graphs indicated by g1
and g2
in stack dat
(or possibly dat2
). Missing values are permitted.
gcor(dat, dat2=NULL, g1=NULL, g2=NULL, diag=FALSE, mode="digraph")
A graph correlation matrix
one or more input graphs.
optionally, a second stack of graphs.
the indices of dat
reflecting the first set of graphs to be compared; by default, all members of dat
are included.
the indices or dat
(or dat2
, if applicable) reflecting the second set of graphs to be compared; by default, all members of dat
are included.
boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the data can contain loops. diag
is FALSE
by default.
string indicating the type of graph being evaluated. "Digraph" indicates that edges should be interpreted as directed; "graph" indicates that edges are undirected. mode
is set to "digraph" by default.
Carter T. Butts buttsc@uci.edu
The (product moment) graph correlation between labeled graphs G and H is given by $$cor(G,H) = \frac{cov(G,H)}{\sqrt{cov(G,G) cov(H,H)}} $$ where the graph covariance is defined as $$cov(G,H) = \frac{1}{{|V| \choose 2}} \sum_{\{i,j\}} \left(A^G_{ij}-\mu_G\right)\left(A^H_{ij}-\mu_H\right)$$ (with \(A^G\) being the adjacency matrix of G). The graph correlation/covariance is at the center of a number of graph comparison methods, including network variants of regression analysis, PCA, CCA, and the like.
Note that gcor
computes only the correlation between uniquely labeled graphs. For the more general case, gscor
is recommended.
Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' CASOS Working Paper, Carnegie Mellon University.
Krackhardt, D. (1987). ``QAP Partialling as a Test of Spuriousness.'' Social Networks, 9, 171-86
gscor
, gcov
, gscov
#Generate two random graphs each of low, medium, and high density
g<-rgraph(10,6,tprob=c(0.2,0.2,0.5,0.5,0.8,0.8))
#Examine the correlation matrix
gcor(g)
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