  Using different methods for laying out a graph can lead to very different
visual appearances, with which the viewer perceives different information.
Selecting a "good" layout method is thus important for visualizing a graph. The
selection can be highly subjective and dependent on the given task. A common
approach to selecting a good layout is to use aesthetic criteria and visual
inspection. However, fully calculating various layouts and their associated
aesthetic metrics is computationally expensive. In this paper, we present a
machine learning approach to large graph visualization based on computing the
topological similarity of graphs using graph kernels. For a given graph, our
approach can show what the graph would look like in different layouts and
estimate their corresponding aesthetic metrics. An important contribution of
our work is the development of a new framework to design graph kernels. Our
experimental study shows that our estimation calculation is considerably faster
than computing the actual layouts and their aesthetic metrics. Also, our graph
kernels outperform the state-of-the-art ones in both time and accuracy. In
addition, we conducted a user study to demonstrate that the topological
similarity computed with our graph kernel matches perceptual similarity
assessed by human users.
