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Select Edges

Source: R/network-utils.R
select_edges.Rd

Selects edges by filter expressions, by node sets, by rank on a metric, or by structural properties such as bridges, communities and reciprocity.

Usage

select_edges(
  x,
  ...,
  top = NULL,
  by = "weight",
  involving = NULL,
  between = NULL,
  bridges_only = FALSE,
  mutual_only = FALSE,
  community = "louvain",
  keep_isolates = TRUE,
  keep_format = FALSE,
  directed = NULL,
  .keep_isolates = NULL
)

Arguments

x

Network input: cograph_network, matrix, igraph, network, or tna object.

...

Filter expressions using edge columns or computed metrics. Available variables:

Edge columns

from, to, weight, plus any custom

Computed metrics

abs_weight, from_degree, to_degree, from_strength, to_strength, edge_betweenness, weight_rank

Predicates

is_bridge, is_mutual (alias is_reciprocal), is_loop, is_multiple, same_community

Endpoint labels

from_label, to_label, from_community, to_community

top

Integer. Select top N edges by a metric.

by

Character. Metric for top selection. Default "weight". Options: "weight", "abs_weight", "edge_betweenness", "from_degree", "to_degree", "from_strength", "to_strength", "weight_rank".

involving

Character or integer. Select edges involving these nodes (by name or index). An edge is selected if either endpoint matches.

between

List of two character/integer vectors. Select edges between two node sets. Example: between = list(c("A", "B"), c("C", "D")).

bridges_only

Logical. Select only bridge edges (edges whose removal disconnects the graph). Default FALSE.

mutual_only

Logical. For directed networks, select only mutual (reciprocated) edges. Default FALSE.

community

Character. Community detection method for same_community variable. One of "louvain", "walktrap", "fast_greedy", "label_prop", "infomap", "leiden". Default "louvain".

keep_isolates

Logical. Keep nodes that end up with no edges? Default TRUE, matching igraph::delete_edges() and tidygraph: filtering edges does not remove nodes. Set FALSE to drop them, or call remove_isolates() afterwards.

keep_format

Logical. If TRUE, matrix, igraph, statnet network and tna inputs are returned in that format. Default FALSE returns cograph_network.

directed

Logical or NULL. If NULL (default), auto-detect.

.keep_isolates

Deprecated. Use keep_isolates.

Value

A cograph_network object with selected edges. If keep_format = TRUE, matrix, igraph, statnet network and tna inputs are converted back to that type. Nodes left without edges are kept and reported in a cograph_isolates_created warning, unless keep_isolates = FALSE.

Details

Selection criteria are combined with AND logic, so an edge is selected only when it satisfies all of them. The top ranking is applied to the edges that pass involving, between, bridges_only and mutual_only, and the filter expressions in ... are applied afterwards. For example, select_edges(x, top = 10, involving = "A") selects the 10 strongest edges among those involving node A.

Only the edge metrics referenced in expressions or required by a selection criterion are computed.

See also

filter_edges, select_nodes, select_bridges, select_top_edges

Examples

select_edges(regulation_net, top = 5, keep_isolates = FALSE)
#> Cograph network: 8 nodes, 5 edges ( directed )
#> Source: matrix 
#>   Nodes (8): Plan, Monitor, Adapt, Reflect, Discuss, Synthesize, Evaluate, Share
#>   Edges: 5 / 56 (density: 8.9%)
#>   Weights: [0.400, 0.490]  |  mean: 0.446
#>   Strongest edges:
#>     Share -> Monitor  0.490
#>     Plan -> Evaluate  0.490
#>     Evaluate -> Adapt  0.430
#>     Synthesize -> Reflect  0.420
#>     Plan -> Discuss  0.400
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.