The computational driver for an important class of graph analysis algorithms is the computation of leading eigenvectors of matrix representations of the graph. In this presentation, we discuss the challenges of calculating eigenvectors of modularity matrices derived from very large graphs (upwards of a billion vertices) and demonstrate the scaling properties of parallel eigensolvers when applied to these matrices.