These functions are wrappers around the various clustering functions provided
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()
The type of component to find. Either
The weight of the edges to use for the calculation. Will be evaluated in the context of the edge data.
Should direction of edges be used for the calculations
The weight of the nodes to use for the calculation. Will be evaluated in the context of the node data.
Number of times partition of the network should be attempted
The initial groups of the nodes. Will be evaluated in the context of the node data.
A logical vector determining which nodes should keep their initial groups. Will be evaluated in the context of the node data.
The number of steps in the random walks
Settings passed on to
arguments passed on to
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
group_edge_betweenness: Group densely connected nodes using
group_fast_greedy: Group nodes by optimising modularity using
group_infomap: Group nodes by minimizing description length using
group_label_prop: Group nodes by propagating labels using
group_leading_eigen: Group nodes based on the leading eigenvector of the modularity matrix using
group_louvain: Group nodes by multilevel optimisation of modularity using
group_optimal: Group nodes by optimising the moldularity score using
group_spinglass: Group nodes using simulated annealing with
group_walktrap: Group nodes via short random walks using
group_biconnected_component: Group edges by their membership of the maximal binconnected components using
#> # 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