This set of functions can be used for calculations that involve node pairs.
If the calculateable measure is not symmetric the function will come in two
flavours, differentiated with `_to`

/`_from`

suffix. The `*_to()`

functions
will take the provided node indexes as the target node (recycling if
necessary). For the `*_from()`

functions the provided nodes are taken as
the source. As for the other wrappers provided, they are intended
for use inside the `tidygraph`

framework and it is thus not necessary to
supply the graph being computed on as the context is known.

```
node_adhesion_to(nodes)
node_adhesion_from(nodes)
node_cohesion_to(nodes)
node_cohesion_from(nodes)
node_distance_to(nodes, mode = "out", weights = NULL, algorithm = "automatic")
node_distance_from(
nodes,
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_cocitation_with(nodes)
node_bibcoupling_with(nodes)
node_similarity_with(nodes, mode = "out", loops = FALSE, method = "jaccard")
node_max_flow_to(nodes, capacity = NULL)
node_max_flow_from(nodes, capacity = NULL)
```

- nodes
The other part of the node pair (the first part is the node defined by the row). Recycled if necessary.

- mode
How should edges be followed? If

`'all'`

all edges are considered, if`'in'`

only inbound edges are considered, and if`'out'`

only outbound edges are considered- weights
The weights to use for calculation

- algorithm
The distance algorithms to use. By default it will try to select the fastest suitable algorithm. Possible values are

`"automatic"`

,`"unweighted"`

,`"dijkstra"`

,`"bellman-ford"`

, and`"johnson"`

- loops
Should loop edges be considered

- method
The similarity measure to calculate. Possible values are:

`"jaccard"`

,`"dice"`

, and`"invlogweighted"`

- capacity
The edge capacity to use

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

`node_adhesion_to()`

: Calculate the adhesion to the specified node. Wraps`igraph::edge_connectivity()`

`node_adhesion_from()`

: Calculate the adhesion from the specified node. Wraps`igraph::edge_connectivity()`

`node_cohesion_to()`

: Calculate the cohesion to the specified node. Wraps`igraph::vertex_connectivity()`

`node_cohesion_from()`

: Calculate the cohesion from the specified node. Wraps`igraph::vertex_connectivity()`

`node_distance_to()`

: Calculate various distance metrics between node pairs. Wraps`igraph::distances()`

`node_distance_from()`

: Calculate various distance metrics between node pairs. Wraps`igraph::distances()`

`node_cocitation_with()`

: Calculate node pair cocitation count. Wraps`igraph::cocitation()`

`node_bibcoupling_with()`

: Calculate node pair bibliographic coupling. Wraps`igraph::bibcoupling()`

`node_similarity_with()`

: Calculate various node pair similarity measures. Wraps`igraph::similarity()`

`node_max_flow_to()`

: Calculate the maximum flow to a node. Wraps`igraph::max_flow()`

`node_max_flow_from()`

: Calculate the maximum flow from a node. Wraps`igraph::max_flow()`

```
# Calculate the distance to the center node
create_notable('meredith') %>%
mutate(dist_to_center = node_distance_to(node_is_center()))
#> # A tbl_graph: 70 nodes and 140 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 70 × 1 (active)
#> dist_to_center
#> <dbl>
#> 1 1
#> 2 1
#> 3 1
#> 4 5
#> 5 6
#> 6 6
#> # … with 64 more rows
#> #
#> # Edge Data: 140 × 2
#> from to
#> <int> <int>
#> 1 1 5
#> 2 1 6
#> 3 1 7
#> # … with 137 more rows
```