Romain Bourqui

Winding roads: routing edges into bundles

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.

Communities and hierarchical structures in dynamic social networks: analysis and visualization

Detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences. Social networks also exhibit dynamic nature as these networks change continuously with the passage of time. Social networks might also present a hierarchical structure led by individuals who play important roles in a society such as managers and decision makers. Detection and visualization of these networks that are changing over time is a challenging problem where communities change as a function of events taking place in the society and the role people play in it. In this paper, we address these issues by presenting a system to analyze dynamic social networks. The proposed system is based on dynamic graph discretization and graph clustering. The system allows detection of major structural changes taking place in social communities over time and reveals hierarchies by identifying influential people in social networks. We use two different data sets for the empirical evaluation and observe that our system helps to discover interesting facts about the social and hierarchical structures present in these social networks.

3D Edge Bundling for Geographical Data Visualization

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.

Detecting Structural Changes and Command Hierarchies in Dynamic Social Networks

Community detection in social networks varying with time is a common yet challenging problem whereby efficient visualization of evolving relationships and implicit hierarchical structure are important task. The main contribution of this paper is towards establishing a framework to analyze such social networks. The proposed framework is based on dynamic graph discretization and graph clustering.The framework allows detection of major structural changes over time, identifies events analyzing temporal dimension and reveals command hierarchies in social networks.We use the Catalano/Vidro dataset for empirical evaluation and observe that our framework provides a satisfactory assessment of the social and hierarchical structure present in the dataset.

How to Draw ClusteredWeighted Graphs using a Multilevel Force-Directed Graph Drawing Algorithm

Visualization of clustered graphs has been a research area since many years. In this paper, we describe a new approach that can be used in real application where graph does not contain only topological information but also extrinsic parameters (i.e. user attributes on edges and nodes). In the case of force-directed algorithm, management of attributes corresponds to take into account edge weights. We propose an extension of the GRIP algorithm in order to manage edge weights. Furthermore, by using Voronoi diagram we constrained that algorithm to draw each cluster in a non overlapping convex region. Using these two extensions we obtained an algorithm that draw clustered weighted graphs. Experimentation has been done on data coming from biology where the network is the genes- proteins interaction graph and where the attributes are gene expression values from microarray experiments.

ImPrEd: an improved force-directed algorithm that prevents nodes from crossing edges

PrEd [ Ber00 ] 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.

Pathway preserving representation of metabolic networks

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.

Large interactive visualization of density functions on big data infrastructure

Point set visualization is required in lots of visualization techniques. Scatter plots as well as geographic heat-maps are straightforward examples. Data analysts are now well trained to use such visualization techniques. The availability of larger and larger datasets raises the need to make these techniques scale as fast as the data grows. The Big Data Infrastructure offers the possibility to scale horizontally. Designing point set visualization methods that fit into that new paradigm is thus a crucial challenge. In this paper, we present a complete architecture which fully fits into the Big Data paradigm and so enables interactive visualization of heatmaps at ultra-scale. A new distributed algorithm for multi-scale aggregation of point set is given and an adaptive GPU based method for kernel density estimation is proposed. A complete prototype working with Hadoop, HBase, Spark and WebGL has been implemented. We give a benchmark of our solution on a dataset having more than 2 billion points.

Visualizing Patterns in Node-link Diagrams

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.

Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks

The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA–mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA–mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks.