  Finding coarse representations of large graphs is an important computational
problem in the fields of scientific computing, large scale graph partitioning,
and the reduction of geometric meshes. Of particular interest in all of these
fields is the preservation of spectral properties with regards to the original
graph. While many methods exist to perform this task, they typically require
expensive linear algebraic operations and yield high work complexities. We
adapt a spectral coarsening bound from the literature in order to develop a
coarsening algorithm with a work complexity that is drastically smaller than
previous work. We further show that this algorithm is easily parallelizable and
presents impressive scaling results on meshes.
