With a graph a single node serving as the starting point, get those nodes in a potential neighborhood of nodes (adjacent to the starting node) that have a common or similar (within threshold values) node attribute to the starting node.

## Usage

get_similar_nbrs(graph, node, node_attr, tol_abs = NULL, tol_pct = NULL)

## Arguments

graph

A graph object of class dgr_graph.

node

A single-length vector containing a node ID value.

node_attr

The name of the node attribute to use to compare with adjacent nodes.

tol_abs

If the values contained in the node attribute node_attr are numeric, one can optionally supply a numeric vector of length 2 that provides a lower and upper numeric bound as criteria for neighboring node similarity to the starting node.

tol_pct

If the values contained in the node attribute node_attr are numeric, one can optionally supply a numeric vector of length 2 that specifies lower and upper bounds as negative and positive percentage changes to the value of the starting node. These bounds serve as criteria for neighboring node similarity to the starting node.

## Value

A vector of node ID values.

## Examples

# Getting similar neighbors can
# be done through numerical comparisons;
# start by creating a random, directed
# graph with 18 nodes and 22 edges
# using the add_gnm_graph() function
graph <-
create_graph() %>%
n = 18,
m = 25,
set_seed = 23) %>%
set_node_attrs(
node_attr = value,
values = rnorm(
n = count_nodes(.),
mean = 5,
sd = 1) %>% round(0))

# Starting with node 10, we
# can test whether any nodes
# numerically equivalent in value
graph %>%
get_similar_nbrs(
node = 10,
node_attr = value)
#> [1]  2 14

# We can also set a tolerance
# for ascribing similarly by using
# either the tol_abs or tol_pct
# arguments (the first applies absolute
# lower and upper bounds from the
# value in the starting node and the
# latter uses a percentage difference
# to do the same); try setting tol_abs
# with a fairly large range to
# determine if several nodes can be
# selected
graph %>%
get_similar_nbrs(
node = 10,
node_attr = value,
tol_abs = c(1, 1))
#> [1]  1  2  9 14

# That resulted in a fairly large
# set of 4 neigboring nodes; for
# sake of example, setting the range
# to be very large will effectively
# return all nodes in the graph
# except for the starting node
graph %>%
get_similar_nbrs(
node = 10,
node_attr = value,
tol_abs = c(10, 10)) %>%
length()
#> [1] 17