These functions are wrappers around the various clustering functions provided
by `igraph`

. As with the other wrappers they automatically use the graph that
is being computed on, and otherwise passes on its arguments to the relevant
clustering function. The return value is always a numeric vector of group
memberships so that nodes or edges with the same number are part of the same
group. Grouping is predominantly made on nodes and currently the only
grouping of edges supported is biconnected components.

```
group_components(type = "weak")
group_edge_betweenness(weights = NULL, directed = TRUE, n_groups = NULL)
group_fast_greedy(weights = NULL, n_groups = NULL)
group_infomap(weights = NULL, node_weights = NULL, trials = 10)
group_label_prop(weights = NULL, label = NULL, fixed = NULL)
group_leading_eigen(
weights = NULL,
steps = -1,
label = NULL,
options = arpack_defaults(),
n_groups = NULL
)
group_louvain(weights = NULL, resolution = 1)
group_leiden(
weights = NULL,
resolution = 1,
objective_function = "CPM",
beta = 0.01,
label = NULL,
n = 2,
node_weights = NULL
)
group_optimal(weights = NULL)
group_spinglass(weights = NULL, ...)
group_walktrap(weights = NULL, steps = 4, n_groups = NULL)
group_fluid(n_groups = 2)
group_biconnected_component()
group_color()
```

- type
The type of component to find. Either

`'weak'`

or`'strong'`

- weights
The weight of the edges to use for the calculation. Will be evaluated in the context of the edge data.

- directed
Should direction of edges be used for the calculations

- n_groups
Integer scalar, the desired number of communities. If too low or two high, then an error message is given. The measure is applied to the full graph so the number of groups returned may be lower for focused graphs

- node_weights
The weight of the nodes to use for the calculation. Will be evaluated in the context of the node data.

- trials
Number of times partition of the network should be attempted

- label
The initial groups of the nodes. Will be evaluated in the context of the node data.

- fixed
A logical vector determining which nodes should keep their initial groups. Will be evaluated in the context of the node data.

- steps
The number of steps in the random walks

- options
Settings passed on to

`igraph::arpack()`

- resolution
Resolution of the modularity function used internally in the algorithm

- objective_function
Either

`"CPM"`

(constant potts model) or`"modularity"`

. Sets the objective function to use.- beta
Parameter affecting the randomness in the Leiden algorithm. This affects only the refinement step of the algorithm.

- n
The number of iterations to run the clustering

- ...
arguments passed on to

`igraph::cluster_spinglass()`

a numeric vector with the membership for each node in the graph. The enumeration happens in order based on group size progressing from the largest to the smallest group

`group_components()`

: Group by connected compenents using`igraph::components()`

`group_edge_betweenness()`

: Group densely connected nodes using`igraph::cluster_edge_betweenness()`

`group_fast_greedy()`

: Group nodes by optimising modularity using`igraph::cluster_fast_greedy()`

`group_infomap()`

: Group nodes by minimizing description length using`igraph::cluster_infomap()`

`group_label_prop()`

: Group nodes by propagating labels using`igraph::cluster_label_prop()`

`group_leading_eigen()`

: Group nodes based on the leading eigenvector of the modularity matrix using`igraph::cluster_leading_eigen()`

`group_louvain()`

: Group nodes by multilevel optimisation of modularity using`igraph::cluster_louvain()`

`group_leiden()`

: Group nodes according to the Leiden algorithm (`igraph::cluster_leiden()`

) which is similar, but more efficient and provides higher quality results than`cluster_louvain()`

`group_optimal()`

: Group nodes by optimising the moldularity score using`igraph::cluster_optimal()`

`group_spinglass()`

: Group nodes using simulated annealing with`igraph::cluster_spinglass()`

`group_walktrap()`

: Group nodes via short random walks using`igraph::cluster_walktrap()`

`group_fluid()`

: Group nodes by simulating fluid interactions on the graph topology using`igraph::cluster_fluid_communities()`

`group_biconnected_component()`

: Group edges by their membership of the maximal binconnected components using`igraph::biconnected_components()`

`group_color()`

: Groups nodes by their color using`igraph::greedy_vertex_coloring()`

. Be aware that this is not a clustering algorithm as coloring specifically provide a color to each node so that no neighbors have the same color

```
create_notable('tutte') %>%
activate(nodes) %>%
mutate(group = group_infomap())
#> # A tbl_graph: 46 nodes and 69 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 46 × 1 (active)
#> group
#> <int>
#> 1 4
#> 2 1
#> 3 1
#> 4 1
#> 5 3
#> 6 3
#> 7 3
#> 8 2
#> 9 2
#> 10 2
#> # ℹ 36 more rows
#> #
#> # Edge Data: 69 × 2
#> from to
#> <int> <int>
#> 1 1 11
#> 2 1 12
#> 3 1 13
#> # ℹ 66 more rows
```