sdsm: Extract backbone using the Stochastic Degree Sequence Model

In backbone: Extracts the Backbone from Graphs

Extract backbone using the Stochastic Degree Sequence Model

Description

sdsm extracts the backbone of a bipartite projection using the Stochastic Degree Sequence Model.

Usage

sdsm(
  B,
  alpha = 0.05,
  missing.as.zero = FALSE,
  signed = FALSE,
  mtc = "none",
  class = "original",
  narrative = FALSE,
  ...
)

Arguments

B	An unweighted bipartite graph, as: (1) an incidence matrix in the form of a matrix or sparse Matrix; (2) an edgelist in the form of a two-column dataframe; (3) an igraph object.
alpha	real: significance level of hypothesis test(s)
missing.as.zero	boolean: should missing edges be treated as edges with zero weight and tested for significance
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 p.adjust.
class	string: the class of the returned backbone graph, one of c("original", "matrix", "Matrix", "igraph", "edgelist"). If "original", the backbone graph returned is of the same class as B.
narrative	boolean: TRUE if suggested text & citations should be displayed.
...	optional arguments

Details

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.

When signed = FALSE, a one-tailed test (is the weight stronger?) is performed for each edge. The resulting backbone contains edges whose weights are significantly stronger than expected in the null model. When signed = TRUE, a two-tailed test (is the weight stronger or weaker?) is performed for each edge. The resulting backbone contains positive edges for those whose weights are significantly stronger, and negative edges for those whose weights are significantly weaker, than expected in the null model.

The bipartite network B may contain some edges that are required in the null model (i.e., structural 1s); these edges should have a weight of 11 (i.e., B_ik = 11). This network may also contain some edges that are prohibited in the null model (i.e., structural 0s); these edges should have a weight of 10 (i.e., B_ik = 10). When B contains required or prohibited edges, cellwise probabilities are computed using logit following Neal et al. (2024). Otherwise, cellwise probabilities are computed using the faster and more accurate Bipartite Configuration Model with bicm (Neal et al. 2021).

Value

If alpha != NULL: Binary or signed backbone graph of class class.

If alpha == NULL: An S3 backbone object containing (1) the weighted graph as a matrix, (2) upper-tail p-values as a matrix, (3, if signed = TRUE) lower-tail p-values as a matrix, (4, if present) node attributes as a dataframe, and (5) several properties of the original graph and backbone model, from which a backbone can subsequently be extracted using backbone.extract().

References

package: Neal, Z. P. (2022). backbone: An R Package to Extract Network Backbones. PLOS ONE, 17, e0269137. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0269137")}

sdsm: 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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.socnet.2014.06.001")}

bicm: 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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41598-021-03238-3")}

logit: Neal, Z. P. and Neal, J. W. (2024). Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC) for Backbone Extraction. Proceedings of the 12th International Conference on Complex Networks and their Applications. Springer.

Examples

#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

backbone documentation built on May 29, 2024, 8:03 a.m.