(self, *args, **kwds)
return VertexClustering(self, membership, modularity)
Calculates the optimal modularity score of the graph and the
        corresponding community structure.

        This function uses the GNU Linear Programming Kit to solve a large
        integer optimization problem in order to find the optimal modularity
        score and the corresponding community structure, therefore it is
        unlikely to work for graphs larger than a few (less than a hundred)
        vertices. Consider using one of the heuristic approaches instead if
        you have such a large graph.

        @return: the calculated membership vector and the corresponding
          modularity in a tuple.
Calculates the optimal modularity score of the graph and the
        corresponding community structure.
Calculates the optimal modularity score of the graph and the corresponding community structure .
def community_optimal_modularity(self, *args, **kwds):
        """Calculates the optimal modularity score of the graph and the
        corresponding community structure.

        This function uses the GNU Linear Programming Kit to solve a large
        integer optimization problem in order to find the optimal modularity
        score and the corresponding community structure, therefore it is
        unlikely to work for graphs larger than a few (less than a hundred)
        vertices. Consider using one of the heuristic approaches instead if
        you have such a large graph.

        @return: the calculated membership vector and the corresponding
          modularity in a tuple."""
        membership, modularity = GraphBase.community_optimal_modularity(
            self, *args, **kwds
        )
        return VertexClustering(self, membership, modularity)
def community_optimal_modularity ( self , * args , * * kwds ) : membership , modularity = GraphBase . community_optimal_modularity ( self , * args , * * kwds ) return VertexClustering ( self , membership , modularity )