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Filter Nodes by Metadata or Centrality

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

Filter nodes using dplyr-style expressions on any node column or centrality measure. Returns a cograph_network object by default (universal format), or optionally the same format as input when keep_format = TRUE.

Usage

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

subset_nodes(
  x,
  ...,
  .keep_edges = c("internal", "none"),
  keep_format = FALSE,
  directed = 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 in the nodes dataframe: id, label, name, x, y, inits, color, plus any custom

Centrality measures

degree, indegree, outdegree, strength, instrength, outstrength, betweenness, closeness, eigenvector, pagerank, hub, authority

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

.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, return the same format as input (matrix returns matrix, igraph returns igraph, etc.). 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.

Value

A cograph_network object with filtered nodes. If keep_format = TRUE, returns the same type as input (matrix, igraph, network, etc.).

See also

filter_edges, splot, subset_nodes

Examples

adj <- matrix(c(0, .5, .8, 0, .5, 0, .3, .6,
                .8, .3, 0, .4, 0, .6, .4, 0), 4, 4, byrow = TRUE)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")

# Keep only high-degree nodes
filter_nodes(adj, degree >= 3)
#> Cograph network: 2 nodes, 1 edges ( undirected )
#> Source: filtered 
#>   Nodes (2): B, C
#>   Edges: 1 / 1 (density: 100.0%)
#>   Weights: [0.300, 0.300]  |  mean: 0.300
#>   Strongest edges:
#>     B -- C  0.300
#> Layout: none 

# Filter by label, combined with degree
filter_nodes(adj, degree >= 2 & label != "D")
#> Cograph network: 3 nodes, 3 edges ( undirected )
#> Source: filtered 
#>   Nodes (3): A, B, C
#>   Edges: 3 / 3 (density: 100.0%)
#>   Weights: [0.300, 0.800]  |  mean: 0.533
#>   Strongest edges:
#>     A -- C  0.800
#>     A -- B  0.500
#>     B -- C  0.300
#> Layout: none