Skip to contents

Select Nodes

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

Selects nodes by name, index, rank on a centrality measure, neighborhood, connected component, or filter expressions on node columns, centrality measures and structural variables.

Usage

select_nodes(
  x,
  ...,
  name = NULL,
  index = NULL,
  top = NULL,
  by = "degree",
  neighbors_of = NULL,
  order = 1L,
  component = NULL,
  keep_edges = c("internal", "none"),
  keep_format = FALSE,
  directed = NULL,
  .keep_edges = NULL
)

Arguments

x

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

...

Filter expressions using node columns, centrality measures, or global context variables. Centrality measures are computed lazily (only those actually referenced). Available variables:

Node columns

All columns of the node table, such as id, label, name, x, y and any custom columns.

Centrality measures

degree, indegree, outdegree, strength, instrength, outstrength, betweenness, closeness, eigenvector, pagerank, hub, authority, coreness. Any other measure centrality() computes can be named too; see list_centralities().

Global context

component, component_size, is_largest_component, neighborhood_size, k_core, is_articulation, is_bridge_endpoint

Predicates

is_isolated, is_source, is_sink, is_leaf, is_cut, local_transitivity, local_triangles

name

Character vector. Select nodes by name/label.

index

Integer vector. Select nodes by index (1-based).

top

Integer. Select top N nodes by centrality measure.

by

Character. Centrality measure for top selection. Default "degree".

neighbors_of

Character or integer. Select neighbors of these nodes (by name or index).

order

Integer. Neighborhood order (1 = direct neighbors, 2 = neighbors of neighbors, etc.). Default 1.

component

Selection mode for connected components:

"largest"

Select nodes in the largest connected component

Integer

Select nodes in component with this ID

Character

Select component containing node with this name

keep_edges

How to handle edges. One of:

"internal"

(default) Keep only edges between remaining nodes

"none"

Remove all edges

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_edges

Deprecated. Use keep_edges.

Value

A cograph_network object with selected nodes. If keep_format = TRUE, matrix, igraph, statnet network and tna inputs are converted back to that type.

Details

Selection criteria are combined with AND logic, so a node is selected only when it satisfies all of them. The top ranking is applied to the nodes that pass name, index, component and neighbors_of, and the filter expressions in ... are applied afterwards. For example, select_nodes(x, top = 10, component = "largest") selects the 10 highest-degree nodes within the largest component.

Only the centrality measures referenced in expressions or in by are computed.

For networks with negative edge weights, betweenness, closeness and pagerank are undefined and return NA, with a cograph_negative_weights warning.

See also

filter_nodes, select_neighbors, select_component, select_top

Examples

select_nodes(regulation_net, top = 3, by = "pagerank")
#> Cograph network: 3 nodes, 3 edges ( directed )
#> Source: matrix 
#>   Nodes (3): Monitor, Reflect, Create
#>   Edges: 3 / 6 (density: 50.0%)
#>   Weights: [0.150, 0.370]  |  mean: 0.230
#>   Strongest edges:
#>     Monitor -> Create  0.370
#>     Create -> Monitor  0.170
#>     Reflect -> Monitor  0.150
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.