FORBID: Fast Overlap Removal By stochastic gradIent Descent for Graph Drawing
This addresses the problem of improving graph visualization readability for users by removing node overlaps, though it is an incremental improvement over existing overlap removal methods.
The paper tackles the problem of node overlaps in graph visualizations, which hinder readability, by proposing a novel overlap removal algorithm that models the task as a joint stress and scaling optimization problem and uses stochastic gradient descent. The result is an efficient method that quickly removes overlaps while preserving the initial layout structures, as demonstrated by comparisons with state-of-the-art algorithms and quality metrics.
While many graph drawing algorithms consider nodes as points, graph visualization tools often represent them as shapes. These shapes support the display of information such as labels or encode various data with size or color. However, they can create overlaps between nodes which hinder the exploration process by hiding parts of the information. It is therefore of utmost importance to remove these overlaps to improve graph visualization readability. If not handled by the layout process, Overlap Removal (OR) algorithms have been proposed as layout post-processing. As graph layouts usually convey information about their topology, it is important that OR algorithms preserve them as much as possible. We propose a novel algorithm that models OR as a joint stress and scaling optimization problem, and leverages efficient stochastic gradient descent. This approach is compared with state-of-the-art algorithms, and several quality metrics demonstrate its efficiency to quickly remove overlaps while retaining the initial layout structures.