Keywords: graph layout, data visualization, representation learning, evaluation
Abstract: Graphs and other structured data have come to the forefront in machine learning over the past few years due to the efficacy of novel representation learning methods boosting prediction performance in various tasks. Representation learning methods embed the nodes in a low-dimensional real-valued space, enabling the application of traditional machine learning methods on graphs. These representations have been widely premised to be also suited for graph visualization. However, no benchmarks or encompassing studies on this topic exist. We present an empirical study comparing several state-of-the-art representation learning methods with two recent graph layout algorithms, using readability and distance-based measures as well as link prediction performance. Generally, no method consistently outperformed the others across quality measures. The graph layout methods provided qualitatively superior layouts when compared to representation learning methods. Embedding graphs in a higher-dimensional space and applying t-Distributed Stochastic Neighbor Embedding for visualization improved the preservation of local neighborhoods, albeit at substantially higher computational cost. A longer version of this paper was recently published in the IEEE Computer Graphics and Applications journal (volume 42, issue 3, 2022). By presenting it at the MLG workshop, we aim to reach the graph machine learning community in addition to the visualization and application-oriented audience of the journal.
Dual Submission: Appeared in the IEEE Computer Graphics and Applications journal (volume 42, issue 3, 2022).