. A projection of topological similarities between 8,263 graphs measured by our RW-LOG-LAPLACIAN kernel. Based on the topological similarity, our approach shows what a graph would look like in different layouts and estimates their corresponding aesthetic metrics. We clustered the graphs based on their topological similarities for the purpose of the user study. The two graphs in each pair are the most topologically similar, but not isomorphic, to each other. The projection is computed with t-SNE [83] , and the highlighted graphs are visualized with FM 3 layout [37] . An interactive plot is available in the supplementary materials [1]. Abstract-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.
Recently, Kwon et al. REF presented a novel work on topological similarity of graphs.
What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
IEEE Transactions on Visualization and Computer Graphics (2018)
Computer Science Mathematics Medicine