def normalisedLaplacianSym(self, outDegree=True, sparse=False):
        W = self.getSparseWeightMatrix()

        if outDegree:
            degrees = numpy.array(W.sum(1)).ravel()
        else:
            degrees = numpy.array(W.sum(1)).ravel()

        L = self.weightMatrixType()((self.getNumVertices(), self.getNumVertices()))
        L.setdiag(numpy.ones(self.getNumVertices()))

        D2 = self.weightMatrixType()((self.getNumVertices(), self.getNumVertices()))
        D2.setdiag((degrees + (degrees==0))**-0.5)

        L = L - D2.dot(W).dot(D2)

        if sparse == True:
            return L
        else:
            return L.toarray()
Compute the normalised symmetric laplacian matrix using L = I - D^-1/2 W D^-1/2,
dataset/ETHPy150Open charanpald/APGL/apgl/graph/SparseGraph.py/SparseGraph.normalisedLaplacianSym