FT_NETWORKANALYSIS computes various network graph measures from
 between-channel or between source-level EEG/MEG signals. This function
 acts as a wrapper aroun the network metrics implemented in the brain
 connectivity toolbox developed by Olaf Sporns and colleagues.

 Use as
   stat = ft_networkanalysis(cfg, data)

 where the first input argument is a configuration structure (see below)
 and the second argument is the output of FT_CONNECTIVITYANALYSIS.

 At present the input data should be channel-level data with dimord
 'chan_chan(_freq)(_time)' or source data with dimord
 'pos_pos(_freq)(_time)'.

 The configuration structure has to contain
   cfg.method    = string, specifying the graph measure that will be
                   computed. See below for the list of supported measures.
   cfg.parameter = string specifying the bivariate parameter in the data
                   for which the graph measure will be computed.

 Supported methods are
   assortativity
   betweenness,      betweenness centrality (nodes)
   charpath,         characteristic path length, needs distance matrix as
                     input
   clustering_coef,  clustering coefficient
   degrees
   density
   distance
   edge_betweenness, betweenness centrality (edges)
   transitivity

 To facilitate data-handling and distributed computing you can use
   cfg.inputfile   =  ...
 	cfg.outputfile  =  ...
 If you specify one of these (or both) the input data will be read from a
 *.mat file on disk and/or the output data will be written to a *.mat
 file. These mat files should contain only a single variable,
 corresponding with the input/output structure.

 See also FT_CONNECTIVITYANALYSIS, FT_CONNECTIVITYPLOT