Abstract cut-and-paste.
In this paper, we develop a cascadic multigrid algorithm for fast computation of the Fiedler vector
of a graph Laplacian, namely, the eigenvector corresponding to the second smallest eigenvalue. This
vector has been found to have applications in fields such as graph partitioning and graph drawing.
The algorithm is a purely algebraic approach based on a heavy edge coarsening scheme and pointwise
smoothing for refinement. To gain theoretical insight, we also consider the related cascadic multigrid
method in the geometric setting for elliptic eigenvalue problems and show its uniform convergence under
certain assumptions. Numerical tests are presented for computing the Fiedler vector of several practical
graphs, and numerical results show the efficiency and optimality of our proposed cascadic multigrid
algorithm.