Filter Nodes by Metadata or Centrality

Filter nodes using dplyr-style expressions on any node column or centrality measure. Returns a cograph_network object by default (universal format), or optionally a matrix, igraph, statnet network or tna object when keep_format = TRUE and the input used one of those formats.

Usage

filter_nodes(
  x,
  ...,
  keep_edges = c("internal", "none"),
  keep_format = FALSE,
  directed = NULL,
  .keep_edges = NULL
)

subset_nodes(
  x,
  ...,
  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 any node column or centrality measure. Available variables include:

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().

Structural context and predicates

The same vocabulary select_nodes() documents, for example component, component_size, k_core, is_isolated, is_cut, local_transitivity.

Examples: degree >= 3, label %in% c("A", "B"), pagerank > 0.1 & degree >= 2.

On a network with negative edge weights, betweenness, closeness and pagerank are undefined: they return NA with a cograph_negative_weights warning.

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 (universal format).

directed

Logical or NULL. If NULL (default), auto-detect from matrix symmetry. Set TRUE to force directed, FALSE to force undirected. Only used for non-cograph_network inputs.

.keep_edges

Deprecated. Use keep_edges.

Value

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

See also

filter_edges, splot, subset_nodes

Examples

filter_nodes(regulation_net, degree >= 7)
#> Cograph network: 3 nodes, 4 edges ( directed )
#> Source: matrix 
#>   Nodes (3): Plan, Monitor, Create
#>   Edges: 4 / 6 (density: 66.7%)
#>   Weights: [0.130, 0.370]  |  mean: 0.217
#>   Strongest edges:
#>     Monitor -> Create  0.370
#>     Plan -> Create  0.200
#>     Create -> Monitor  0.170
#>     Plan -> Monitor  0.130
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.