  Drawing large graphs appropriately is an important step for the visual
analysis of data from real-world networks. Here we present a novel multilevel
algorithm to compute a graph layout with respect to a recently proposed metric
that combines layout stress and entropy. As opposed to previous work, we do not
solve the linear systems of the maxent-stress metric with a typical numerical
solver. Instead we use a simple local iterative scheme within a multilevel
approach. To accelerate local optimization, we approximate long-range forces
and use shared-memory parallelism. Our experiments validate the high potential
of our approach, which is particularly appealing for dynamic graphs. In
comparison to the previously best maxent-stress optimizer, which is sequential,
our parallel implementation is on average 30 times faster already for static
graphs (and still faster if executed on one thread) while producing a
comparable solution quality.
