Large Quasi-Tree Drawing: A Neighborhood Based Approach

International audienceIn this paper, we present an algorithm to lay out a particular class of graphs coming from real case studies: the quasi-tree graph class. Protein and internet mappings projects have shown the interest of devicing dedicated tools for visualizing such graphs. Our method addresses a challenging problem which consists in computing a layout of large graphs (up to hundred of thousands of nodes) that emphasizes their tree-like property in an efficient time. In order to validate our approach, we compare our results on real data to those obtained by well known algorithms

Winding Roads: Routing edges into bundles

International audienceVisualizing 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

rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining

Data description. Data description and availability, and parameter settings used in this study. (PDF 101 kb

Un algorithme stable de décomposition pour l'analyse des réseaux sociaux dynamiques

National audienceDynamical networks raise new analysis problems. An efficient analysis tool has to not only allow to split the network into groups of \og similar~\fg{} elements but also allow to detect changes in the network structure. In this article, we describe a new method for analyzing such dynamical networks. This technique is based on an algorithm of decomposition of graph into overlapping clusters. Time complexity of this algorithm is $O(|E| \cdot deg_{max}^2 + |V| \cdot log(|V|)))$. The stability of that algorithm allows to detect the changes of the studied network over the time

A stable decomposition algorithm for dynamical social network analysis

Dynamic networks raise new knowledge discovery challenges. To handle efficiently this kind of data, an analysis method has to both decompose the network (modelled by a graph) into similar set of nodes and let the user detect structural changes in the graph. In this article we present a graph decomposition algorithm generating overlapping clusters. The complexity of this algorithmis O(|E|·deg_max^2+|V| · log(|V|))). This algorithm is particularly efficient due to its ability to detect major modifications along dynamic processes such as time related ones

Pathway Preserving Representation of Metabolic Networks

International audienceImprovements 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 informa- tion 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 place- ment 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

3D Edge Bundling for Geographical Data Visualization

International audienceVisualization 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 po- sitions are fixed, such as in geography were nodes posi- tions are set according to geographical coordinates. Edge bundling techniques can help to solve this issue by visu- ally 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 pa- per, we present a generalization of [18] to reduce the clut- ter 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

ImPrEd: An Improved Force-Directed Algorithm that Prevents Nodes from Crossing Edges

International audiencePrEd is a force-directed algorithm that improves the existing layout of a graph while preserving its edge crossing properties. The algorithm has a number of applications including: improving the layouts of planar graph drawing algorithms, interacting with a graph layout, and drawing Euler-like diagrams. The algorithm ensures that nodes do not cross edges during its execution. However, PrEd can be computationally expensive and overly-restrictive in terms of node movement. In this paper, we introduce ImPrEd: an improved version of PrEd that overcomes some of its limitations and widens its range of applicability. ImPrEd also adds features such as flexible or crossable edges, allowing for greater control over the output. Flexible edges, in particular, can improve the distribution of graph elements and the angular resolution of the input graph. They can also be used to generate Euler diagrams with smooth boundaries. As flexible edges increase data set size, we experience an execution/drawing quality trade off. However, when flexible edges are not used, ImPrEd proves to be consistently faster than PrEd