cluster_fast_greedy
Community structure via greedy optimization of modularity
This function tries to find dense subgraph, also called communities in graphs via directly optimizing a modularity score.
- Keywords
- graphs
Usage
cluster_fast_greedy(graph, merges = TRUE, modularity = TRUE,
membership = TRUE, weights = E(graph)$weight)Arguments
- graph
The input graph
- merges
Logical scalar, whether to return the merge matrix.
- modularity
Logical scalar, whether to return a vector containing the modularity after each merge.
- membership
Logical scalar, whether to calculate the membership vector corresponding to the maximum modularity score, considering all possible community structures along the merges.
- weights
If not
NULL, then a numeric vector of edge weights. The length must match the number of edges in the graph. By default the ‘weight’ edge attribute is used as weights. If it is not present, then all edges are considered to have the same weight. Larger edge weights correspond to stronger connections.
Details
This function implements the fast greedy modularity optimization algorithm for finding community structure, see A Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://www.arxiv.org/abs/cond-mat/0408187 for the details.
Value
cluster_fast_greedy returns a communities
object, please see the communities manual page for details.
References
A Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://www.arxiv.org/abs/cond-mat/0408187
See Also
communities for extracting the results.
See also cluster_walktrap,
cluster_spinglass,
cluster_leading_eigen and
cluster_edge_betweenness for other methods.
Examples
# NOT RUN {
g <- make_full_graph(5) %du% make_full_graph(5) %du% make_full_graph(5)
g <- add_edges(g, c(1,6, 1,11, 6, 11))
fc <- cluster_fast_greedy(g)
membership(fc)
sizes(fc)
# }