Abstract
In the context of graph layout, many algorithms have been designed to remove node overlapping, and many quality criteria and associated met-rics have been proposed to evaluate those algorithms. Unfortunately, a complete comparison of the algorithms based on some metrics that evaluate their quality has never been provided and it is thus difficult for a visualisation designer to select the algorithm that best suits their needs. In this paper, we review 22 metrics available in the literature, classify them according to the quality criteria they try to capture, and select a representative one for each class. Based on the selected metrics,we compare 9 node overlap removal algorithms. Our experiment involves 854 synthetic and real-world graphs. Finally, we propose a JavaScript library containing both the algorithms and the criteria, and we provide a Web platform, AGORA, in which one can upload graphs, apply the algorithms and compare/download the results.
Highlights
Graph-drawing algorithms are good at creating rich expressive graph layouts but often consider nodes as points with no dimensions
Our contribution comes in three forms: (1) We propose a classification of 22 quality metrics, grouping them according to the quality criterion they try to capture
(3) We present a JavaScript library2, that contains all the algorithms described in this paper, and a Web platform, AGORA3 (Automatic Graph Overlap Removal Algorithms), in which one can upload a set of graphs, apply the node overlap removal algorithms and download the results and the values of the quality criteria4
Summary
Graph-drawing algorithms are good at creating rich expressive graph layouts but often consider nodes as points with no dimensions. Post-processing algorithms, named layout adjustment [21], have been proposed to remove node overlap. The objective of these algorithms is, given an initial positioning of the nodes and a size for each one, to provide a new embedding so that there are no overlapping nodes any more. Our contribution comes in three forms: (1) We propose a classification of 22 quality metrics, grouping them according to the quality criterion they try to capture. We discuss their relevance and we select a representative one for each class.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
