Spectral concentration and greedy k-clustering

A popular graph clustering method is to consider the embedding of an input graph into Rk induced by the first k eigenvectors of its Laplacian, and to partition the graph via geometric manipulations on the resulting metric space. Despite the practical success of this methodology, there is limited understanding of several heuristics that follow this framework. We provide theoretical justification for one such natural and computationally efficient variant.