EVOLUTION-MANAGER
Edit File: cluster_fast_greedy.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Community structure via greedy optimization of modularity</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for cluster_fast_greedy {igraph}"><tr><td>cluster_fast_greedy {igraph}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Community structure via greedy optimization of modularity</h2> <h3>Description</h3> <p>This function tries to find dense subgraph, also called communities in graphs via directly optimizing a modularity score. </p> <h3>Usage</h3> <pre> cluster_fast_greedy( graph, merges = TRUE, modularity = TRUE, membership = TRUE, weights = NULL ) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>graph</code></td> <td> <p>The input graph</p> </td></tr> <tr valign="top"><td><code>merges</code></td> <td> <p>Logical scalar, whether to return the merge matrix.</p> </td></tr> <tr valign="top"><td><code>modularity</code></td> <td> <p>Logical scalar, whether to return a vector containing the modularity after each merge.</p> </td></tr> <tr valign="top"><td><code>membership</code></td> <td> <p>Logical scalar, whether to calculate the membership vector corresponding to the maximum modularity score, considering all possible community structures along the merges.</p> </td></tr> <tr valign="top"><td><code>weights</code></td> <td> <p>The weights of the edges. It must be a positive numeric vector, <code>NULL</code> or <code>NA</code>. If it is <code>NULL</code> and the input graph has a ‘weight’ edge attribute, then that attribute will be used. If <code>NULL</code> and no such attribute is present, then the edges will have equal weights. Set this to <code>NA</code> if the graph was a ‘weight’ edge attribute, but you don't want to use it for community detection. A larger edge weight means a stronger connection for this function.</p> </td></tr> </table> <h3>Details</h3> <p>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. </p> <h3>Value</h3> <p><code>cluster_fast_greedy</code> returns a <code><a href="communities.html">communities</a></code> object, please see the <code><a href="communities.html">communities</a></code> manual page for details. </p> <h3>Author(s)</h3> <p>Tamas Nepusz <a href="mailto:ntamas@gmail.com">ntamas@gmail.com</a> and Gabor Csardi <a href="mailto:csardi.gabor@gmail.com">csardi.gabor@gmail.com</a> for the R interface. </p> <h3>References</h3> <p>A Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://www.arxiv.org/abs/cond-mat/0408187 </p> <h3>See Also</h3> <p><code><a href="communities.html">communities</a></code> for extracting the results. </p> <p>See also <code><a href="cluster_walktrap.html">cluster_walktrap</a></code>, <code><a href="cluster_spinglass.html">cluster_spinglass</a></code>, <code><a href="cluster_leading_eigen.html">cluster_leading_eigen</a></code> and <code><a href="cluster_edge_betweenness.html">cluster_edge_betweenness</a></code>, <code><a href="cluster_louvain.html">cluster_louvain</a></code> <code><a href="cluster_leiden.html">cluster_leiden</a></code> for other methods. </p> <h3>Examples</h3> <pre> 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) </pre> <hr /><div style="text-align: center;">[Package <em>igraph</em> version 1.3.5 <a href="00Index.html">Index</a>]</div> </body></html>