data visualization. The second package is a complete OpenGL rendering engine ( Tulip OGL ) tailored for information visualization techniques. The third package is a library of GUI components created using the Qt library ( Tulip GUI ). The fourth package is a complete binding of the C++ API to Python. It makes the usage of Tulip inside Python code straightforward. Finally, Tulip software is an application where users can embed their algorithm, visualization technique, or complete information visualization pipeline. Figure 2 summarized the connections between these different libraries.

Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on a decade of research and development of this framework, we present the architecture, consisting of a suite of tools and techniques, that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems he or she is addressing. The current framework enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. The software model facilitates the reuse of components and allows the developers to focus on programming their application. This development pipeline makes the framework efficient for research prototyping as well as the development of end-user applications.

Résumé. The Graph Visualization Framework Tulip now enjoys 10 years of user experience, and has matured its architecture and development cycle. Originally designed to interactively navigate large graphs, the framework integrates state-of-the-art software engineering concepts and good practices. It offers a large panel of graphical representations (traditional graph drawing as well as alternate representations). Tulip is most useful in a data mining and knowledge discovery context, allowing users to easily add their own data analysis and computing routines through its plug-in architecture.

Cet article decrit une etude de cas exhibant les qualites de la plateforme de visualisation de graphes Tulip, demontrant l'apport de la visualisation a la fouille de donnees interactive et a l'extraction de connaissances. Le calcul dSun graphe a partir d'indices de similarite est un exemple typique ou l'exploration visuelle et interactive de graphes vient en appui au travail de fouille de donnees. Nous penchons sur le cas ou l'on souhaite etudier une collection de documents afin d'avoir une idee des thematiques abordees dans la collection.

Background The tools that are available to draw and to manipulate the representations of metabolism are usually restricted to metabolic pathways. This limitation becomes problematic when studying processes that span several pathways. The various attempts that have been made to draw genome-scale metabolic networks are confronted with two shortcomings: 1- they do not use contextual information which leads to dense, hard to interpret drawings, 2- they impose to fit to very constrained standards, which implies, in particular, duplicating nodes making topological analysis considerably more difficult. Results We propose a method, called MetaViz, which enables to draw a genome-scale metabolic network and that also takes into account its structuration into pathways. This method consists in two steps: a clustering step which addresses the pathway overlapping problem and a drawing step which consists in drawing the clustered graph and each cluster. Conclusion The method we propose is original and addresses new drawing issues arising from the no-duplication constraint. We do not propose a single drawing but rather several alternative ways of presenting metabolism depending on the pathway on which one wishes to focus. We believe that this provides a valuable tool to explore the pathway structure of metabolism.

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.