This set of functions provide wrappers to a number of `ìgraph`

s graph
statistic algorithms. 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. All of these
functions are guarantied to return scalars making it easy to compute with
them.

```
graph_adhesion()
graph_assortativity(attr, in_attr = NULL, directed = TRUE)
graph_automorphisms(sh = "fm", colors = NULL)
graph_clique_num()
graph_clique_count(min = NULL, max = NULL, subset = NULL)
graph_component_count(type = "weak")
graph_motif_count(size = 3, cut.prob = rep(0, size))
graph_diameter(weights = NULL, directed = TRUE, unconnected = TRUE)
graph_girth()
graph_radius(mode = "out")
graph_mutual_count()
graph_asym_count()
graph_unconn_count()
graph_size()
graph_order()
graph_reciprocity(ignore_loops = TRUE, ratio = FALSE)
graph_min_cut(capacity = NULL)
graph_mean_dist(directed = TRUE, unconnected = TRUE, weights = NULL)
graph_modularity(group, weights = NULL)
graph_efficiency(weights = NULL, directed = TRUE)
```

- attr
The node attribute to measure on

- in_attr
An alternative node attribute to use for incomming node. If

`NULL`

the attribute given by`type`

will be used- directed
Should a directed graph be treated as directed

- sh
The splitting heuristics for the BLISS algorithm. Possible values are: ‘

`f`

’: first non-singleton cell, ‘`fl`

’: first largest non-singleton cell, ‘`fs`

’: first smallest non-singleton cell, ‘`fm`

’: first maximally non-trivially connected non-singleton cell, ‘`flm`

’: first largest maximally non-trivially connected non-singleton cell, ‘`fsm`

’: first smallest maximally non-trivially connected non-singleton cell.- colors
The colors of the individual vertices of the graph; only vertices having the same color are allowed to match each other in an automorphism. When omitted, igraph uses the

`color`

attribute of the vertices, or, if there is no such vertex attribute, it simply assumes that all vertices have the same color. Pass NULL explicitly if the graph has a`color`

vertex attribute but you do not want to use it.- min, max
The upper and lower bounds of the cliques to be considered.

- subset
The indexes of the nodes to start the search from (logical or integer). If provided only the cliques containing these nodes will be counted.

- type
The type of component to count, either 'weak' or 'strong'. Ignored for undirected graphs.

- size
The size of the motif.

- cut.prob
Numeric vector giving the probabilities that the search graph is cut at a certain level. Its length should be the same as the size of the motif (the

`size`

argument). By default no cuts are made.- weights
Optional positive weight vector for calculating weighted distances. If the graph has a

`weight`

edge attribute, then this is used by default.- unconnected
Logical, what to do if the graph is unconnected. If FALSE, the function will return a number that is one larger the largest possible diameter, which is always the number of vertices. If TRUE, the diameters of the connected components will be calculated and the largest one will be returned.

- mode
How should eccentricity be calculated. If

`"out"`

only outbound edges are followed. If`"in"`

only inbound are followed. If`"all"`

all edges are followed. Ignored for undirected graphs.- ignore_loops
Logical. Should loops be ignored while calculating the reciprocity

- ratio
Should the old "ratio" approach from igraph < v0.6 be used

- capacity
The capacity of the edges

- group
The node grouping to calculate the modularity on

A scalar, the type depending on the function

`graph_adhesion()`

: Gives the minimum edge connectivity. Wraps`igraph::edge_connectivity()`

`graph_assortativity()`

: Measures the propensity of similar nodes to be connected. Wraps`igraph::assortativity()`

`graph_automorphisms()`

: Calculate the number of automorphisms of the graph. Wraps`igraph::count_automorphisms()`

`graph_clique_num()`

: Get the size of the largest clique. Wraps`igraph::clique_num()`

`graph_clique_count()`

: Get the number of maximal cliques in the graph. Wraps`igraph::count_max_cliques()`

`graph_component_count()`

: Count the number of unconnected componenets in the graph. Wraps`igraph::count_components()`

`graph_motif_count()`

: Count the number of motifs in a graph. Wraps`igraph::count_motifs()`

`graph_diameter()`

: Measures the length of the longest geodesic. Wraps`igraph::diameter()`

`graph_girth()`

: Measrues the length of the shortest circle in the graph. Wraps`igraph::girth()`

`graph_radius()`

: Measures the smallest eccentricity in the graph. Wraps`igraph::radius()`

`graph_mutual_count()`

: Counts the number of mutually connected nodes. Wraps`igraph::dyad_census()`

`graph_asym_count()`

: Counts the number of asymmetrically connected nodes. Wraps`igraph::dyad_census()`

`graph_unconn_count()`

: Counts the number of unconnected node pairs. Wraps`igraph::dyad_census()`

`graph_size()`

: Counts the number of edges in the graph. Wraps`igraph::gsize()`

`graph_order()`

: Counts the number of nodes in the graph. Wraps`igraph::gorder()`

`graph_reciprocity()`

: Measures the proportion of mutual connections in the graph. Wraps`igraph::reciprocity()`

`graph_min_cut()`

: Calculates the minimum number of edges to remove in order to split the graph into two clusters. Wraps`igraph::min_cut()`

`graph_mean_dist()`

: Calculates the mean distance between all node pairs in the graph. Wraps`igraph::mean_distance()`

`graph_modularity()`

: Calculates the modularity of the graph contingent on a provided node grouping`graph_efficiency()`

: Calculate the global efficiency of the graph

```
# Use e.g. to modify computations on nodes and edges
create_notable('meredith') %>%
activate(nodes) %>%
mutate(rel_neighbors = centrality_degree()/graph_order())
#> # A tbl_graph: 70 nodes and 140 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 70 × 1 (active)
#> rel_neighbors
#> <dbl>
#> 1 0.0571
#> 2 0.0571
#> 3 0.0571
#> 4 0.0571
#> 5 0.0571
#> 6 0.0571
#> 7 0.0571
#> 8 0.0571
#> 9 0.0571
#> 10 0.0571
#> # 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
```