mark_select: Marking nodes for selection based on measures

In manynet: Many Ways to Make, Modify, Map, Mark, and Measure Myriad Networks

Marking nodes for selection based on measures

Description

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

• node_is_random() marks one or more nodes at random.

• 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.

Usage

node_is_random(.data, size = 1)

node_is_max(node_measure, ranks = 1)

node_is_min(node_measure, ranks = 1)

node_is_mean(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).
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.

See Also

Other marks: mark_diff, mark_nodes, mark_tie_select, mark_ties, mark_triangles

Examples

node_is_random(ison_brandes, 2)
#node_is_max(migraph::node_degree(ison_brandes))
#node_is_min(migraph::node_degree(ison_brandes))
#node_is_mean(node_degree(ison_brandes))

manynet documentation built on June 23, 2025, 9:07 a.m.