(self, lanczosVecs = 15, maxiter = 20)
return _np.abs(evals_sorted[1])
Returns the algebraic connectivity of the higher-order network.    
        
        @param lanczosVecs: number of Lanczos vectors to be used in the approximate
            calculation of eigenvectors and eigenvalues. This maps to the ncv parameter 
            of scipy's underlying function eigs. 
        @param maxiter: scaling factor for the number of iterations to be used in the 
            approximate calculation of eigenvectors and eigenvalues. The number of iterations 
            passed to scipy's underlying eigs function will be n*maxiter where n is the
            number of rows/columns of the Laplacian matrix.
Returns the algebraic connectivity of the higher-order network.
Returns the algebraic connectivity of the higher - order network .
def getAlgebraicConnectivity(self, lanczosVecs = 15, maxiter = 20):
        """
        Returns the algebraic connectivity of the higher-order network.    
        
        @param lanczosVecs: number of Lanczos vectors to be used in the approximate
            calculation of eigenvectors and eigenvalues. This maps to the ncv parameter 
            of scipy's underlying function eigs. 
        @param maxiter: scaling factor for the number of iterations to be used in the 
            approximate calculation of eigenvectors and eigenvalues. The number of iterations 
            passed to scipy's underlying eigs function will be n*maxiter where n is the
            number of rows/columns of the Laplacian matrix.         
        """
    
        Log.add('Calculating algebraic connectivity ... ', Severity.INFO)

        L = self.getLaplacianMatrix()
        # NOTE: ncv sets additional auxiliary eigenvectors that are computed
        # NOTE: in order to be more confident to find the one with the largest
        # NOTE: magnitude, see https://github.com/scipy/scipy/issues/4987
        w = _sla.eigs( L, which="SM", k=2, ncv=lanczosVecs, return_eigenvectors=False, maxiter = maxiter )
        evals_sorted = _np.sort(_np.absolute(w))

        Log.add('finished.', Severity.INFO)

        return _np.abs(evals_sorted[1])
def getAlgebraicConnectivity ( self , lanczosVecs = 15 , maxiter = 20 ) : Log . add ( 'Calculating algebraic connectivity ... ' , Severity . INFO ) L = self . getLaplacianMatrix ( ) # NOTE: ncv sets additional auxiliary eigenvectors that are computed # NOTE: in order to be more confident to find the one with the largest # NOTE: magnitude, see https://github.com/scipy/scipy/issues/4987 w = _sla . eigs ( L , which = "SM" , k = 2 , ncv = lanczosVecs , return_eigenvectors = False , maxiter = maxiter ) evals_sorted = _np . sort ( _np . absolute ( w ) ) Log . add ( 'finished.' , Severity . INFO ) return _np . abs ( evals_sorted [ 1 ] )