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)

group_fast_greedy(weights = 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 = igraph::arpack_defaults
)

group_louvain(weights = NULL)

group_optimal(weights = NULL)

group_spinglass(weights = NULL, ...)

group_walktrap(weights = NULL, steps = 4)

group_biconnected_component()

Arguments

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

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()

...

arguments passed on to igraph::cluster_spinglass()

Value

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

Functions

Examples

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 x 1 (active) #> group #> <int> #> 1 4 #> 2 3 #> 3 3 #> 4 3 #> 5 1 #> 6 1 #> # … with 40 more rows #> # #> # Edge Data: 69 x 2 #> from to #> <int> <int> #> 1 1 11 #> 2 1 12 #> 3 1 13 #> # … with 66 more rows