Abstract. Interaction is a vital component in the visualization of multivariate networks. It enables greater amounts of information to be seen and explored than is possible with static visualization. Interaction can also help show the information landscape of the data while still allowing users to find and view areas of interest in greater detail and pivot between these. In this chapter we first discuss the design space and requirements for interacting with large multivariate data sets. We describe and classify relevant interaction techniques, and give examples of the interactive aspects of multivariate graph visualization systems. We present recommendations and guidelines for designing novel interaction approaches. Finally, we describe the open challenges within the field of multivariate graph visualization as we see them.

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Abstract-The amount of data produced in the world every day implies a huge challenge in understanding and extracting knowledge from it. Much of this data is of relational nature, such as social networks, metabolic pathways, or links between software components. Traditionally, those networks are represented as node-link diagrams or matrix representations. They help us to understand the structure (topology) of the relational data. However in many real world data sets, additional (often multidimensional) attributes are attached to the network elements. One challenge is to show these attributes in context of the underlying network topology in order to support the user in further analyses. In this paper, we present a novel approach that extends traditional force-based graph layouts to create an attribute-driven layout. In addition, our prototype implementation supports interactive exploration by introducing clustering and multidimensional scaling into the analysis process.

The visual analysis of complex networks is a challenging task in many fields, such as systems biology or social sciences. Often, various domain experts work together to improve the analysis time or the quality of the analysis results. Collaborative visualization tools can facilitate the analysis process in such situations. We propose a new web-based visualization environment which supports distributed, synchronous and asynchronous collaboration. In addition to standard collaboration features like event tracking or synchronizing, our client/server-based system provides a rich set of visualization and interaction techniques for better navigation and overview of the input network. Changes made by specific analysts or even just visited network elements are highlighted on demand by heat maps. They enable us to visualize user behavior data without affecting the original graph visualization, are robust against layout changes, and are user-sensitive in a sense that the current analyst is able to perceive which changes were made by others in asynchronous collaboration. In case of synchronous collaboration, an analyst can see where and what others are currently analyzing in the network visualization. Thus, our approach addresses critical collaborative visualization challenges, for instance, awareness and coordination of user activities or pointing to interesting objects. We evaluated the usability of the heat map approach against two alternatives in a controlled user experiment. In addition, the results of a domain expert review are described in this article.

Abstract. The advancements of web technologies in recent years made it possible to switch from traditional desktop software to online solutions. Today, people naturally use web applications to work together on documents, spreadsheets, or blogs in real time. Also interactive data visualizations are more and more shared in the web. They are thus easily accessible, and it is possible to collaboratively discuss and explore complex data sets. A still open problem in collaborative information visualization is the online exploration of node-link diagrams of graphs (or networks) in fields such as social sciences or systems biology. In this paper, we address challenges related to this research problem and present a client/server-based visualization system for the collaborative exploration of graphs. Our approach uses WebGL to render large graphs in a web application and provides tools to coordinate the analysis process of multiple users in synchronous as well as asynchronous sessions.