Realistic interaction networks usually present two main properties: a power-law degree distribution and a small world behavior. Few nodes are linked to many nodes and adjacent nodes are likely to share common neighbors. Moreover, graph structure usually presents a dense core that is difficult to explore with classical filtering and clustering techniques. In this paper, we propose a new filtering technique accounting for a user-focus. This technique extracts a tree-like graph with also power-law degree distribution and small world behavior. Resulting structure is easily drawn with classical force-directed drawing algorithms. It is also quickly clustered and displayed into a multi-level silhouette tree (MuSi-Tree) from any user-focus. We built a new graph filtering + clustering + drawing API and report a case study.

International audienceIn this paper, we present a new graphical interface for traditional library environments, which allows the user to elaborate easily and efficiently new strategies in search processes. This tool is based on two linked interactive Euler diagram representations. The first one is an interactive representation of the structures composing the documentary kernel of the library. The user may navigate and select items, making their own understanding of the database content, structure and access. The second one is a set based visualization of the results of a composed query. This allows the user to validate his search context and to elaborate strategies to go through the results. The association of both interfaces generates a tool that allows the user to elaborate the main search strategies through graphical manipulations

With the increase of online resources, one main challenge for multimedia content providers is to provide efficient and user friendly tools for a deep and shallow navigation adapted to large scale audiovisual content. This paper describes a generic framework to build visual interactive applications the objectives of which are to enhance the understanding and to allow easy access to multimedia resources and management. Visual Maps are built on multi-modal similarity matrices computed from automatically extracted descriptors and use graph clustering and layout methods. Active relevance feedback methods are applied to allow users to control the maps evolution according to their needs. The First results of users' evaluation are presented for one of our tools.

We present an interactive visualization, called Table Of Video Contents (TOVC), for browsing structured TV programs such as news, magazines or sports. In these telecasts, getting a good segmentation can be very time-consuming, especially in an annotating context. This visualization, connected with a classical media player, offers a very handy video browser. This system allows a global overview by showing the temporal structure and by giving some semantic information. The drawn structure enables a non linear video access by suggesting relevant frames. The TOVC is created from a graphic framework designed for computing similarities on visual content, and displaying the associated proximities in a 2D map with graph representation. TOVC is one of its first applications and shows interesting capabilities.

Abstract. Several hierarchical clustering techniques have been proposed to visualize large graphs, but fewer solutions suggest a focus based approach. We propose a multilevel clustering technique that produces in linear time a contextual clustered view depending on a user-focus. We get a tree of clusters where each cluster -called meta-silhouette -is itself hierarchically clustered into an inclusion tree of silhouettes. Resulting Multilevel Silhouette Tree (MuSi-Tree) has a specific structure called multilevel compound tree. This work builds upon previous work on a compound tree structure called MO-Tree. The work presented in this paper is a major improvement over previous work by (1) defining multilevel compound tree as a more generic structure, (2) proposing original space-filling visualization techniques to display it, (3) defining relevant interaction model based on both focus changes and graph filtering techniques and (4) reporting from case studies in various fields: co-citation graphs, relateddocument graphs and social graphs.