.de TQ
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.  ns
.  TP \\$1
..
.TH CLUSTER 1 "3 March 2011"
.SH NAME
cluster \- find clusters in a graph and augment the graph with this information.
.SH SYNOPSIS
.B cluster
[\fB\-v?\fP]
[
.BI \-C k
]
[
.BI \-c k
]
[
.B \-o
.I outfile
]
[ 
.I files
]
.SH DESCRIPTION
.B cluster
takes as input a graph in DOT format, finds node clusters and augments 
the graph with this information. 
The clusters are specified by the "cluster" attribute attached to nodes; cluster
values are non-negative integers.
.B cluster
attempts to maximize the modularity of the clustering.
If the edge attribute "weight" is defined, this will be used in 
computing the clustering.
.SH OPTIONS
The following options are supported:
.TP
.BI \-C k 
specifies a targeted number of clusters that should be generated.
The specified number \fIk\fP is only a suggestion and may not be realisable. 
If \fIk == 0\fP, the default, the number of clusters that approximately optimizes the modularity is returned.
.TP
.BI \-c k
specifies clustering method.
If \fIk == 0\fP, the default, modularity clustering will be used. 
If \fIk == 1\fP modularity quality will be used.
.TP
.BI \-o outfile 
Specifies that output should go into the file \fIoutfile\fP. By default,
\fIstdout\fP is used.
.TP
.BI \-v 
Verbose mode.
.SH EXAMPLES
.PP
Applying 
.B cluster
to the following graph,
.PP
.nf
\fB   graph {
       1--2 [weight=10.]
       2--3 [weight=1]
       3--4 [weight=10.]
       4--5 [weight=10]
       5--6 [weight=10]
       3--6 [weight=0.1]
       4--6 [weight=10.]
      }\fP
.fi
.PP
gives
.PP
.nf
\fB   graph {
         node [cluster="-1"];
         1 [cluster=1];
         2 [cluster=1];
         3 [cluster=2];
         4 [cluster=2];
         5 [cluster=2];
         6 [cluster=2];
         1 -- 2 [weight="10."];
         2 -- 3 [weight=1];
         3 -- 4 [weight="10."];
         4 -- 5 [weight=10];
         5 -- 6 [weight=10];
         3 -- 6 [weight="0.1"];
         4 -- 6 [weight="10."];
   }\fP
.fi


.SH AUTHOR
Yifan Hu <yifanhu@yahoo.com>
.SH "SEE ALSO"
.PP
gvmap(1)
.PP

Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (2008), P10008.