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       cluster - find clusters in a graph and augment the graph with this information.


       cluster [-v?]  [ -Ck ] [ -ck ] [ -o outfile ] [ files ]


       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.   cluster  attempts  to maximize the
       modularity of the clustering.  If the edge attribute "weight" is  defined,  this  will  be
       used in computing the clustering.


       The following options are supported:

       -Ck    specifies  a  targeted  number of clusters that should be generated.  The specified
              number k is only a suggestion and may not be realisable.  If k == 0,  the  default,
              the number of clusters that approximately optimizes the modularity is returned.

       -ck    specifies clustering method.  If k == 0, the default, modularity clustering will be
              used.  If k == 1 modularity quality will be used.

              Specifies that output should go into the file outfile. By default, stdout is used.

       -v     Verbose mode.


       Applying cluster to the following graph,

          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.]


          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."];


       Yifan Hu <>



       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.

                                           3 March 2011                                CLUSTER(1)