Fabien Jourdan

Learn More
Many networks under study in information visualization are "small world" networks. These networks first appeared in the study of social networks and were shown to be relevant models in other application domains such as software reverse engineering and biology. Furthermore, many of these networks actually have a multiscale nature: they can be viewed as a(More)
High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of(More)
We present a multiscale MDS method extending Chalmers' pivot-based MDS algorithm (Morrison et al., 2003). Our multi-scale strategy is itself based on a 0(N log N) hybrid MDS approach. Our algorithm clearly improves over its predecessors with respect to time, while producing layouts of a comparable quality.
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(More)
We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography-mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with(More)
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(More)
Insight of multiscale networks could be accessed through the visualization of automatic multiscale clusterings. But results of these methods do not necessarily fulfill user expectations since they don't provide error prone clusterings. In this article we propose a way to refine interactively these results by the use of multiscale grouping and ungrouping(More)
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(More)