| sdsm {backbone} | R Documentation |
sdsm extracts the backbone of a bipartite projection using the Stochastic Degree Sequence Model.
sdsm( B, method = "RefinedNormal", alpha = 0.05, signed = FALSE, mtc = "none", class = "original", narrative = FALSE, ... )
B |
An unweighted bipartite graph, as: (1) an incidence matrix in the form of a matrix or sparse |
method |
string: Specifies the method of the Poisson Binomial distribution computation used by the "ppbinom" function in PoissonBinomial-Distribution. "RefinedNormal" gives quick, very accurate approximations, while "DivideFFT" gives the quickest exact computations. |
alpha |
real: significance level of hypothesis test(s) |
signed |
boolean: TRUE for a signed backbone, FALSE for a binary backbone (see details) |
mtc |
string: type of Multiple Test Correction to be applied; can be any method allowed by |
class |
string: the class of the returned backbone graph, one of c("original", "matrix", "sparseMatrix", "igraph", "network", "edgelist").
If "original", the backbone graph returned is of the same class as |
narrative |
boolean: TRUE if suggested text & citations should be displayed. |
... |
optional arguments |
The sdsm function compares an edge's observed weight in the projection B*t(B) to the distribution of weights
expected in a projection obtained from a random bipartite network where both the row vertex degrees and column
vertex degrees are approximately fixed at their values in B. It uses the Bipartite Configuration Model bicm
to compute probabilities for the Poisson binomial distribution.
When signed = FALSE, a one-tailed test (is the weight stronger) is performed for each edge with a non-zero weight. It
yields a backbone that perserves edges whose weights are significantly stronger than expected in the chosen null
model. When signed = TRUE, a two-tailed test (is the weight stronger or weaker) is performed for each every pair of nodes.
It yields a backbone that contains positive edges for edges whose weights are significantly stronger, and
negative edges for edges whose weights are significantly weaker, than expected in the chosen null model.
NOTE: Before v2.0.0, all significance tests were two-tailed and zero-weight edges were evaluated.
If alpha != NULL: Binary or signed backbone graph of class class.
If alpha == NULL: An S3 backbone object containing three matrices (the weighted graph, edges' upper-tail p-values,
edges' lower-tail p-values), and a string indicating the null model used to compute p-values, from which a backbone
can subsequently be extracted using backbone.extract(). The signed, mtc, class, and narrative parameters
are ignored.
Neal, Z. P. (2014). The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance, and other co-behaviors. Social Networks, 39, 84-97. doi: 10.1016/j.socnet.2014.06.001 Neal, Z. P., Domagalski, R., and Sagan, B. (2021). Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections. Scientific Reports, 11, 23929. doi: 10.1038/s41598-021-03238-3
#A binary bipartite network of 30 agents & 75 artifacts; agents form three communities
B <- rbind(cbind(matrix(rbinom(250,1,.8),10),
matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.2),10)),
cbind(matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.8),10),
matrix(rbinom(250,1,.2),10)),
cbind(matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.8),10)))
P <- B%*%t(B) #An ordinary weighted projection...
plot(igraph::graph_from_adjacency_matrix(P, mode = "undirected",
weighted = TRUE, diag = FALSE)) #...is a dense hairball
bb <- sdsm(B, alpha = 0.05, narrative = TRUE, class = "igraph") #An SDSM backbone...
plot(bb) #...is sparse with clear communities