Group nodes and edges based on community structure
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()
type |
The type of component to find. Either |
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 |
... |
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 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_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_biconnected_component: Group edges by their membership of the maximal binconnected components using igraph::biconnected_components()
create_notable('tutte') %>%
activate(nodes) %>%
mutate(group = group_infomap())Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.