mlf: Extract backbone using the Marginal Likelihood Filter

In backbone: Extracts the Backbone from Graphs

Extract backbone using the Marginal Likelihood Filter

Description

mlf extracts the backbone of a weighted network using the Marginal Likelihood Filter

Usage

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

Arguments

W	An integer-weighted unipartite graph, as: (1) an adjacency matrix in the form of a matrix or sparse Matrix; (2) an edgelist in the form of a three-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 W.
narrative	boolean: TRUE if suggested text & citations should be displayed.

Details

The mlf function applies the marginal likelihood filter (MLF; Dianati, 2016), which compares an edge's weight to its expected weight in a graph that preserves the total weight and preserves the degree sequence on average. The graph may be directed or undirected, however the edge weights must be positive integers.

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.

If W is an unweighted bipartite graph, then the MLF is applied to its weighted bipartite projection.

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")}

mlf: Dianati, N. (2016). Unwinding the hairball graph: Pruning algorithms for weighted complex networks. Physical Review E, 93, 012304. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1103/PhysRevE.93.012304")}

Examples

#A network with heterogeneous weights
net <- matrix(c(0,10,10,10,10,75,0,0,0,0,
                10,0,1,1,1,0,0,0,0,0,
                10,1,0,1,1,0,0,0,0,0,
                10,1,1,0,1,0,0,0,0,0,
                10,1,1,1,0,0,0,0,0,0,
                75,0,0,0,0,0,100,100,100,100,
                0,0,0,0,0,100,0,10,10,10,
                0,0,0,0,0,100,10,0,10,10,
                0,0,0,0,0,100,10,10,0,10,
                0,0,0,0,0,100,10,10,10,0),10)

net <- igraph::graph_from_adjacency_matrix(net, mode = "undirected", weighted = TRUE)
plot(net, edge.width = sqrt(igraph::E(net)$weight)) #A stronger clique & a weaker clique

strong <- igraph::delete_edges(net, which(igraph::E(net)$weight < mean(igraph::E(net)$weight)))
plot(strong) #A backbone of stronger-than-average edges ignores the weaker clique

bb <- mlf(net, alpha = 0.05, narrative = TRUE) #An MLF backbone...
plot(bb) #...preserves edges at multiple scales

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