These functions wraps a set of functions that all measures quantities of the local neighborhood of each node. They all return a vector or list matching the node position.

```
local_size(order = 1, mode = "all", mindist = 0)
local_members(order = 1, mode = "all", mindist = 0)
local_triangles()
local_ave_degree(weights = NULL)
local_transitivity(weights = NULL)
```

- order
Integer giving the order of the neighborhood.

- mode
Character constant, it specifies how to use the direction of the edges if a directed graph is analyzed. For ‘out’ only the outgoing edges are followed, so all vertices reachable from the source vertex in at most

`order`

steps are counted. For ‘"in"’ all vertices from which the source vertex is reachable in at most`order`

steps are counted. ‘"all"’ ignores the direction of the edges. This argument is ignored for undirected graphs.- mindist
The minimum distance to include the vertex in the result.

- weights
An edge weight vector. For

`local_ave_degree`

: If this argument is given, the average vertex strength is calculated instead of vertex degree. For`local_transitivity`

: if given weighted transitivity using the approach by*A. Barrat*will be calculated.

A numeric vector or a list (for `local_members`

) with elements
corresponding to the nodes in the graph.

`local_size()`

: The size of the neighborhood in a given distance from the node. (Note that the node itself is included unless`mindist > 0`

). Wraps`igraph::ego_size()`

.`local_members()`

: The members of the neighborhood of each node in a given distance. Wraps`igraph::ego()`

.`local_triangles()`

: The number of triangles each node participate in. Wraps`igraph::count_triangles()`

.`local_ave_degree()`

: Calculates the average degree based on the neighborhood of each node. Wraps`igraph::knn()`

.`local_transitivity()`

: Calculate the transitivity of each node, that is, the propensity for the nodes neighbors to be connected. Wraps`igraph::transitivity()`

```
# Get all neighbors of each graph
create_notable('chvatal') %>%
activate(nodes) %>%
mutate(neighborhood = local_members(mindist = 1))
#> # A tbl_graph: 12 nodes and 24 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 12 × 1 (active)
#> neighborhood
#> <list>
#> 1 <int [4]>
#> 2 <int [4]>
#> 3 <int [4]>
#> 4 <int [4]>
#> 5 <int [4]>
#> 6 <int [4]>
#> # … with 6 more rows
#> #
#> # Edge Data: 24 × 2
#> from to
#> <int> <int>
#> 1 6 7
#> 2 7 8
#> 3 8 9
#> # … with 21 more rows
# These are equivalent
create_notable('chvatal') %>%
activate(nodes) %>%
mutate(n_neighbors = local_size(mindist = 1),
degree = centrality_degree()) %>%
as_tibble()
#> # A tibble: 12 × 2
#> n_neighbors degree
#> <dbl> <dbl>
#> 1 4 4
#> 2 4 4
#> 3 4 4
#> 4 4 4
#> 5 4 4
#> 6 4 4
#> 7 4 4
#> 8 4 4
#> 9 4 4
#> 10 4 4
#> 11 4 4
#> 12 4 4
```