These functions are a collection of node measures that do not really fall
into the class of centrality measures. For lack of a better place they are
collected under the `node_*`

umbrella of functions.

```
node_eccentricity(mode = "out")
node_constraint(weights = NULL)
node_coreness(mode = "out")
node_diversity(weights)
node_efficiency(weights = NULL, directed = TRUE, mode = "all")
node_bridging_score()
node_effective_network_size()
node_connectivity_impact()
node_closeness_impact()
node_fareness_impact()
```

- mode
How edges are treated. In

`node_coreness()`

it chooses which kind of coreness measure to calculate. In`node_efficiency()`

it defines how the local neighborhood is created- weights
The weights to use for each node during calculation

- directed
Should the graph be treated as a directed graph if it is in fact directed

A numeric vector of the same length as the number of nodes in the graph.

`node_eccentricity()`

: measure the maximum shortest path to all other nodes in the graph`node_constraint()`

: measures Burts constraint of the node. See`igraph::constraint()`

`node_coreness()`

: measures the coreness of each node. See`igraph::coreness()`

`node_diversity()`

: measures the diversity of the node. See`igraph::diversity()`

`node_efficiency()`

: measures the local efficiency around each node. See`igraph::local_efficiency()`

`node_bridging_score()`

: measures Valente's Bridging measures for detecting structural bridges (`influenceR`

)`node_effective_network_size()`

: measures Burt's Effective Network Size indicating access to structural holes in the network (`influenceR`

)`node_connectivity_impact()`

: measures the impact on connectivity when removing the node (`NetSwan`

)`node_closeness_impact()`

: measures the impact on closeness when removing the node (`NetSwan`

)`node_fareness_impact()`

: measures the impact on fareness (distance between all node pairs) when removing the node (`NetSwan`

)

```
# Calculate Burt's Constraint for each node
create_notable('meredith') %>%
mutate(b_constraint = node_constraint())
#> # A tbl_graph: 70 nodes and 140 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 70 × 1 (active)
#> b_constraint
#> <dbl>
#> 1 0.25
#> 2 0.25
#> 3 0.25
#> 4 0.25
#> 5 0.25
#> 6 0.25
#> 7 0.25
#> 8 0.25
#> 9 0.25
#> 10 0.25
#> # ℹ 60 more rows
#> #
#> # Edge Data: 140 × 2
#> from to
#> <int> <int>
#> 1 1 5
#> 2 1 6
#> 3 1 7
#> # ℹ 137 more rows
```