EVOLUTION-MANAGER
Edit File: cluster_fluid_communities.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 detection algorithm based on interacting fluids</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_fluid_communities {igraph}"><tr><td>cluster_fluid_communities {igraph}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Community detection algorithm based on interacting fluids</h2> <h3>Description</h3> <p>The algorithm detects communities based on the simple idea of several fluids interacting in a non-homogeneous environment (the graph topology), expanding and contracting based on their interaction and density. </p> <h3>Usage</h3> <pre> cluster_fluid_communities(graph, no.of.communities) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>graph</code></td> <td> <p>The input graph. The graph must be simple and connected. Empty graphs are not supported as well as single vertex graphs. Edge directions are ignored. Weights are not considered.</p> </td></tr> <tr valign="top"><td><code>no.of.communities</code></td> <td> <p>The number of communities to be found. Must be greater than 0 and fewer than number of vertices in the graph.</p> </td></tr> </table> <h3>Value</h3> <p><code>cluster_fluid_communities</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>Ferran Parés </p> <h3>References</h3> <p>Parés F, Gasulla DG, et. al. (2018) Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm. In: Complex Networks &amp; Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), Springer, vol 689, p 229, doi: 10.1007/978-3-319-72150-7_19 </p> <h3>See Also</h3> <p>See <code><a href="communities.html">communities</a></code> for extracting the membership, modularity scores, etc. from the results. </p> <p>Other community detection algorithms: <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>, <code><a href="cluster_edge_betweenness.html">cluster_edge_betweenness</a></code>, <code><a href="cluster_fast_greedy.html">cluster_fast_greedy</a></code>, <code><a href="cluster_label_prop.html">cluster_label_prop</a></code> <code><a href="cluster_louvain.html">cluster_louvain</a></code>, <code><a href="cluster_leiden.html">cluster_leiden</a></code> </p> <h3>Examples</h3> <pre> g <- graph.famous("Zachary") comms <- cluster_fluid_communities(g, 2) </pre> <hr /><div style="text-align: center;">[Package <em>igraph</em> version 1.3.5 <a href="00Index.html">Index</a>]</div> </body></html>