anchor.diff:      % communities in the graph as it stands initially.  Separate
       % out the computation of these values as an optimization since
       % they are also needed to do the paritioning.
  -    Weights = weights(GD),
  -    Modularity = modularity(Weights),
   %   file:write_file("/tmp/part", io_lib:format("starting communities: ~p~n", [lists:sort(dict:to_list(GD#louvain_graphd.communitiesd))])),
       #louvain_dendrogram{
  -                louvain_graphs = partition(GD, Weights, Modularity, [])}.
  +                louvain_graphs = partition(GD, [])}.
   
   %% ----------------------------------------------------------------------------
   %% Local Functions
  
positive.code:             % nodes, edges weighted accordingly), and recurse.
              CommunityGD = community_graph(PartitionedGD),
              partition(CommunityGD,
                        weights(CommunityGD),
                        NewModularity,
                        [graph(PartitionedGD) | L])
      end.
  
  
negative.code:          communities/1,
           dendrogram/1]).
  
  -define(MIN_MODULARITY_CHANGE, 0.001).
  
  -include("part_louvain.hrl").
  -include("tap_logger.hrl").
  