The emergence of very large hierarchies that result from the increase in available data raises many problems of visualization and navigation. On data sets of such scale, classical graph drawing methods do not take advantage of certain human cognitive skills such as shape recognition. These cognitive skills could make it easier to remember the global structure of the data. In this paper, we propose a method that is based on the use of nested irregular shapes. We name it GosperMap as we rely on the use of a Gosper Curve to generate these shapes. By employing human perception mechanisms that were developed by handling, for example, cartographic maps, this technique facilitates the visualization and navigation of a hierarchy. An algorithm has been designed to preserve region containment according to the hierarchy and to set the leaves' sizes proportionally to a property, in such a way that the size of nonleaf regions corresponds to the sum of their children's sizes. Moreover, the input ordering of the hierarchy's nodes is preserved, i.e., the areas that represent two consecutive children of a node in the hierarchy are adjacent to one another. This property is especially useful because it guarantees some stability in our algorithm. We illustrate our technique by providing visualization examples of the repartition of tax money in the US over time. Furthermore, we validate the use of the GosperMap in a professional documentation context and show the stability and ease of memorization for this type of map.

Pattern discovery plays an important part in the graph analysis process. Good examples are the detection of communities in social networks or the clustering into pathways of metabolic networks. However, elements may be shared by several clusters, making the patterns entangled. When mining such data, experts are usually interested in both each individual cluster and their overlaps. Dedicated visualization methods are therefore necessary to efficiently support their exploration process. In this article, we propose a new method that emphasizes patterns in a node-link diagram representation and allows to easily identify overlaps between these patterns as well. Our technique combines graph topology and embedding to compute concave hulls with holes surrounding the patterns of interest.

Abstract Improvements in biological data acquisition and genomes sequencing now allow to reconstruct entire metabolic networks of many living organisms. The size and complexity of these networks prohibit manual drawing and thereby urge the need of dedicated visualization techniques. An efficient representation of such a network should preserve the topological information of metabolic pathways while respecting biological drawing conventions. These constraints complicate the automatic generation of such visualization as it raises graph drawing issues. In this paper we propose a method to lay out the entire metabolic network while preserving the pathway information as much as possible. That method is flexible as it enables the user to define whether or not node duplication should be performed, to preserve or not the network topology. Our technique combines partitioning, node placement and edge bundling to provide a pseudo‐orthogonal visualization of the metabolic network. To ease pathway information retrieval, we also provide complementary interaction tools that emphasize relevant pathways in the entire metabolic context.

This paper describes an approach exploiting the full capabilities of GPU's to enhance the usability of edge bundling in real applications. Edge bundling, as well as other edge clustering approaches relying on the use of high quality edge rerouting. Typical approach for drawing edge-bundled graph is to render edges as curves. But curves generation can have a relatively high computational costs and do not easily comply with real-time interaction. Furthermore, while edge bundling provides a much better overall readability of a graph, the bundles make it more difficult to recover local information. Our goal was thus to provide fluid interaction allowing the recovery of local information through specific interaction techniques. The system we built offers folklore or classical interaction such as zoom & pan, fish-eye and magnifying lens. We also implemented the Bring & Go technique by Tominski et al. We proposed an approach exploiting the full computing power of GPU's when rendering graph edges as parametric splines. The gain in efficiency when running all curves computations on the GPU turns bundling techniques into techniques that can be embedded in interactive systems concerned with graphs of several thousands of nodes and edges.

Visualization of graphs containing many nodes and edges efficiently is quite challenging since representations generally suffer from visual clutter induced by the large amount of edge crossings and node-edge overlaps. That problem becomes even more important when nodes positions are fixed, such as in geography were nodes positions are set according to geographical coordinates. Edge bundling techniques can help to solve this issue by visually merging edges along common routes but it can also help to reveal high-level edge patterns in the network and therefore to understand its overall organization. In this paper, we present a generalization of [18] to reduce the clutter in a 3D representation by routing edges into bundles as well as a GPU-based rendering method to emphasize bundles densities while preserving edge color. To visualize geographical networks in the context of the globe, we also provide a new technique allowing to bundle edges around and not across it.

Abstract Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently it is difficult to read the relationships between nodes and the high‐level edge patterns that may exist in standard node‐link diagram representations. Edge bundling techniques have been proposed to help solve this issue, which rely on high quality edge rerouting. In this paper, we introduce an intuitive edge bundling technique which efficiently reduces edge clutter in graphs drawings. Our method is based on the use of a grid built using the original graph to compute the edge rerouting. In comparison with previously proposed edge bundling methods, our technique improves both the level of clutter reduction and the computation performance. The second contribution of this paper is a GPU‐based rendering method which helps users perceive bundles densities while preserving edge color.