Marking nodes based on their properties

Source: R/mark_nodes.R

These functions return logical vectors the length of the nodes in a network identifying which hold certain properties.

node_is_cutpoint() and node_is_isolate() are useful for identifying nodes that are in particular positions in the network. More can be added here.

node_is_max() and node_is_min() are more generally useful for converting the results from some node measure into a mark-class object. They can be particularly useful for highlighting which node or nodes are key because they minimise or, more often, maximise some measure.

node_is_cutpoint(.data)

node_is_isolate(.data)

node_is_core(.data)

node_is_random(.data, size = 1)

node_is_mentor(.data, elites = 0.1)

node_is_max(node_measure, ranks = 1)

node_is_min(node_measure, ranks = 1)

Arguments

.data

An object of a {manynet}-consistent class:

  • matrix (adjacency or incidence) from {base} R

  • edgelist, a data frame from {base} R or tibble from {tibble}

  • igraph, from the {igraph} package

  • network, from the {network} package

  • tbl_graph, from the {tidygraph} package

size

The number of nodes to select (as TRUE).

elites

The proportion of nodes to be selected as mentors. By default this is set at 0.1. This means that the top 10% of nodes in terms of degree, or those equal to the highest rank degree in the network, whichever is the higher, will be used to select the mentors.

Note that if nodes are equidistant from two mentors, they will choose one at random. If a node is without a path to a mentor, for example because they are an isolate, a tie to themselves (a loop) will be created instead. Note that this is a different default behaviour than that described in Valente and Davis (1999).

node_measure

An object created by a node_ measure.

ranks

The number of ranks of max or min to return. For example, ranks = 3 will return TRUE for nodes with scores equal to any of the top (or, for node_is_min(), bottom) three scores. By default, ranks = 1.

Functions

  • node_is_cutpoint(): Returns logical of which nodes cut or act as articulation points in a network, increasing the number of connected components in a graph when removed.

  • node_is_isolate(): Returns logical of which nodes are isolates, with neither incoming nor outgoing ties.

  • node_is_core(): Returns logical of which nodes are members of the core of the network.

  • node_is_random(): Returns a logical vector indicating a random selection of nodes as TRUE.

  • node_is_mentor(): Returns a logical vector indicating mentor (high indegree) nodes as TRUE.

  • node_is_max(): Returns logical of which nodes hold the maximum of some measure

  • node_is_min(): Returns logical of which nodes hold the minimum of some measure

References

Valente, Thomas, and Rebecca Davis. 1999. "Accelerating the Diffusion of Innovations Using Opinion Leaders", Annals of the American Academy of Political and Social Science 566: 56-67.

See also

Other marks: is(), mark_ties

Examples

node_is_cutpoint(ison_brandes)
#>   V1    V2    V3    V4    V5    V6    V7    V8    V9    V10   V11  
#> 1 FALSE FALSE TRUE  TRUE  FALSE FALSE FALSE FALSE TRUE  FALSE FALSE
node_is_isolate(ison_brandes)
#>   V1    V2    V3    V4    V5    V6    V7    V8    V9    V10   V11  
#> 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
node_is_core(ison_brandes)
#>   V1    V2    V3    V4    V5    V6    V7    V8    V9    V10   V11  
#> 1 FALSE FALSE TRUE  TRUE  FALSE FALSE FALSE FALSE TRUE  FALSE FALSE
node_is_random(ison_brandes, 2)
#>   V1    V2    V3    V4    V5    V6    V7    V8    V9    V10   V11  
#> 1 FALSE FALSE FALSE FALSE FALSE FALSE TRUE  FALSE FALSE TRUE  FALSE
node_is_max(node_degree(ison_brandes))
#>   V1    V2    V3    V4    V5    V6    V7    V8    V9    V10   V11  
#> 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE  FALSE FALSE
node_is_min(node_degree(ison_brandes))
#>   V1    V2    V3    V4    V5    V6    V7    V8    V9    V10   V11  
#> 1 TRUE  TRUE  FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE  TRUE