
Linear layouts are a simple and natural way to draw a graph: all vertices are placed on a single line and edges are drawn as arcs between the vertices. Despite its simplicity, a linear layout can be a very meaningful visualization if there is a particular order defined on the vertices. Common examples of such ordered - and often also directed - graphs are event sequences and processes: public transport systems tracking passenger check-in and check-out, banks checking online transactions, or hospitals recording the paths of patients through their system, to name a few. A main drawback of linear layouts are the usually (very) large aspect ratios of the resulting drawings, which prevent users from obtaining a good overview of the whole graph. In this paper we present a novel and versatile algorithm to optimally fold a linear layout of a graph such that it can be drawn effectively in a specified aspect ratio, while still clearly communicating the linearity of the layout.
Graph/Network Data, Geometry based Techniques, Folding, Linear layouts, F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems, Routing and layout
Graph/Network Data, Geometry based Techniques, Folding, Linear layouts, F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems, Routing and layout
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
