group_graph
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.
Usage
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
group_components: Group by connected compenents usingigraph::components()group_edge_betweenness: Group densely connected nodes usingigraph::cluster_edge_betweenness()group_fast_greedy: Group nodes by optimising modularity usingigraph::cluster_fast_greedy()group_infomap: Group nodes by minimizing description length usingigraph::cluster_infomap()group_label_prop: Group nodes by propagating labels usingigraph::cluster_label_prop()group_leading_eigen: Group nodes based on the leading eigenvector of the modularity matrix usingigraph::cluster_leading_eigen()group_louvain: Group nodes by multilevel optimisation of modularity usingigraph::cluster_louvain()group_optimal: Group nodes by optimising the moldularity score usingigraph::cluster_optimal()group_spinglass: Group nodes using simulated annealing withigraph::cluster_spinglass()group_walktrap: Group nodes via short random walks usingigraph::cluster_walktrap()group_biconnected_component: Group edges by their membership of the maximal binconnected components usingigraph::biconnected_components()
Examples
# NOT RUN {
create_notable('tutte') %>%
activate(nodes) %>%
mutate(group = group_infomap())
# }