What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
This addresses the computational expense of graph visualization for users needing efficient layout selection, though it is incremental as it builds on existing graph kernel methods.
The paper tackles the problem of selecting good graph layouts by presenting a machine learning approach that uses graph kernels to estimate aesthetic metrics and visual appearances without full computation, achieving faster estimation and outperforming state-of-the-art kernels in time and accuracy.
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.