disparity | R Documentation |

`disparity`

extracts the backbone of a weighted network using the Disparity Filter.

disparity( W, alpha = 0.05, signed = FALSE, mtc = "none", class = "original", narrative = FALSE )

`W` |
A weighted unipartite graph, as: (1) an adjacency matrix in the form of a matrix or sparse |

`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", "Matrix", "igraph", "edgelist").
If "original", the backbone graph returned is of the same class as |

`narrative` |
boolean: TRUE if suggested text & citations should be displayed. |

The `disparity`

function applies the disparity filter (Serrano et al., 2009), which compares an edge's weight to
its expected weight if a node's total degree was uniformly distributed across all its edges. The graph may be
directed or undirected, however the edge weights must be positive.

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 `W`

is an unweighted bipartite graph, any rows and columns that contain only zeros or only ones are removed, then
the global threshold is applied to its weighted bipartite projection.

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()`

.

package: Neal, Z. P. (2022). backbone: An R Package to Extract Network Backbones. *PLOS ONE, 17*, e0269137. doi: 10.1371/journal.pone.0269137

disparity filter: Serrano, M. A., Boguna, M., & Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. *Proceedings of the National Academy of Sciences, 106*, 6483-6488. doi: 10.1073/pnas.0808904106

#A network with heterogeneous (i.e. multiscale) 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 <- disparity(net, alpha = 0.05, narrative = TRUE) #A disparity backbone... plot(bb) #...preserves edges at multiple scales

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.