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    Metadata details:
    filename: community_louvain.m
    path of file: ./repos/EEGapp/EEGapp/custom_stuff/BCT/2017_01_15_BCT
    
    the code of the file until where you have to start completion: function [M,Q]=community_louvain(W,gamma,M0,B)
%COMMUNITY_LOUVAIN     Optimal community structure
%
%   M     = community_louvain(W);
%   [M,Q] = community_louvain(W,gamma);
%   [M,Q] = community_louvain(W,gamma,M0);
%   [M,Q] = community_louvain(W,gamma,M0,'potts');
%   [M,Q] = community_louvain(W,gamma,M0,'negative_asym');
%   [M,Q] = community_louvain(W,[],[],B);
%
%   The optimal community structure is a subdivision of the network into
%   nonoverlapping groups of nodes which maximizes the number of within-
%	group edges, and minimizes the number of between-group edges.
%
%   This function is a fast and accurate multi-iterative generalization of
%   the Louvain community detection algorithm. This function subsumes and
%   improves upon,
%		modularity_louvain_und.m, modularity_finetune_und.m,
%		modularity_louvain_dir.m, modularity_finetune_dir.m,
%       modularity_louvain_und_sign.m
%	and additionally allows to optimize other objective functions (includes
%	built-in Potts-model Hamiltonian, allows for custom objective-function
%	matrices).
%
%   Inputs:
%       W,
%           directed/undirected weighted/binary connection matrix with
%           positive and possibly negative weights.
%       gamma,
%           resolution parameter (optional)
%               gamma>1,        detects smaller modules
%               0<=gamma<1,     detects larger modules
%               gamma=1,        classic modularity (default)
%       M0,
%           initial community affiliation vector (optional)
%       B,

    
function [M,Q]=community_louvain(W,gamma,M0,B)
%COMMUNITY_LOUVAIN     Optimal community structure
%
%   M     = community_louvain(W);
%   [M,Q] = community_louvain(W,gamma);
%   [M,Q] = community_louvain(W,gamma,M0);
%   [M,Q] = community_louvain(W,gamma,M0,'potts');
%   [M,Q] = community_louvain(W,gamma,M0,'negative_asym');
%   [M,Q] = community_louvain(W,[],[],B);
%
%   The optimal community structure is a subdivision of the network into
%   nonoverlapping groups of nodes which maximizes the number of within-
%	group edges, and minimizes the number of between-group edges.
%
%   This function is a fast and accurate multi-iterative generalization of
%   the Louvain community detection algorithm. This function subsumes and
%   improves upon,
%		modularity_louvain_und.m, modularity_finetune_und.m,
%		modularity_louvain_dir.m, modularity_finetune_dir.m,
%       modularity_louvain_und_sign.m
%	and additionally allows to optimize other objective functions (includes
%	built-in Potts-model Hamiltonian, allows for custom objective-function
%	matrices).
%
%   Inputs:
%       W,
%           directed/undirected weighted/binary connection matrix with
%           positive and possibly negative weights.
%       gamma,
%           resolution parameter (optional)
%               gamma>1,        detects smaller modules
%               0<=gamma<1,     detects larger modules
%               gamma=1,        classic modularity (default)
%       M0,
%           initial community affiliation vector (optional)
%       B,
%           objective-function type or custom objective matrix (optional)
%           'modularity',       modularity (default)
%           'potts',            Potts-model Hamiltonian (for binary networks)
%           'negative_sym',     symmetric treatment of negative weights
%           'negative_asym',    asymmetric treatment of negative weights
%           B,                  custom objective-function matrix
%
%           Note: see Rubinov and Sporns (2011) for a discussion of
%           symmetric vs. asymmetric treatment of negative weights.
%
%   Outputs:
%       M,
%           community affiliation vector
%       Q,
%           optimized community-structure statistic (modularity by default)
%
%   Example:
%       % Iterative community finetuning.
%       % W is the input connection matrix.
%       n  = size(W,1);             % number of nodes
%       M  = 1:n;                   % initial community affiliations
%       Q0 = -1; Q1 = 0;            % initialize modularity values
%       while Q1-Q0>1e-5;           % while modularity increases
%           Q0 = Q1;                % perform community detection
%           [M, Q1] = community_louvain(W, [], M);
%       end