Our goal is to define a list of tasks for graph visualization that has enough detail and specificity to be useful to: 1) designers who want to improve their system and 2) to evaluators who want to compare graph visualization systems. In this paper, we suggest a list of tasks we believe are commonly encountered while analyzing graph data. We define graph specific objects and demonstrate how all complex tasks could be seen as a series of low-level tasks performed on those objects. We believe that our taxonomy, associated with benchmark datasets and specific tasks, would help evaluators generalize results collected through a series of controlled experiments.

Abstract-The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.

MatrixExplorer is a network visualization system that uses two representations: node-link diagrams and matrices. Its design comes from a list of requirements formalized after several interviews and a participatory design session conducted with social science researchers. Although matrices are commonly used in social networks analysis, very few systems support the matrix-based representations to visualize and analyze networks. MatrixExplorer provides several novel features to support the exploration of social networks with a matrix-based representation, in addition to the standard interactive filtering and clustering functions. It provides tools to reorder (layout) matrices, to annotate and compare findings across different layouts and find consensus among several clusterings. MatrixExplorer also supports Node-link diagram views which are familiar to most users and remain a convenient way to publish or communicate exploration results. Matrix and node-link representations are kept synchronized at all stages of the exploration process.

Abstract. Visualizing social networks presents challeges for both nodelink and adjacency matrix representations. Social networks are locally dense, which makes node-link displays unreadable. Yet, main analysis tasks require following paths, which is difficult on matrices. This article presents MatLink, a hybrid representation with links overlaid on the borders of a matrix and dynamic topological feedback as the pointer moves. We evaluated MatLink by an experiment comparing its readability, in term of errors and time, for social network-related tasks to the other conventional representations on graphs varying in size (small and medium) and density. It showed significant advantages for most tasks, especially path-related ones where standard matrices are weak.

INRIA Figure 1: A protein-protein interaction dataset (100,000 nodes and 1,000,000 edges) visualized using ZAME at two different levels of zoom. ABSTRACTWe present the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higher-order metarepresentations. Aggregates are arranged into a pyramid hierarchy that allows for on-demand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia-over 500,000 articles and 6,000,000 links-with interactive performance on standard consumer-level computer hardware.

Applications supporting navigation in large networks are used every days by millions of people. They include road map navigators, ight route visualization systems, and network visualization systems using node-link diagrams. These applications currently provide generic interaction methods for navigation: pan-and-zoom and sometimes bird's eye views.This article explores the idea of exploiting the connection information provided by the network to help navigate these large spaces. We visually augment two traditional navigation methods, and develop two special-purpose techniques. The first new technique, called "Link Sliding", provides guided panning when continuously dragging along a visible link. The second technique, called "Bring & Go", brings adjacent nodes nearby when pointing to a node. We compare the performance of these techniques in both an adjacency exploration task and a node revisiting task. This comparison illustrates the various advantages of content-aware network navigation techniques. A significant speed advantage is found for the Bring & Go technique over other methods.

We present a visual exploration of the field of human-computer interaction through the author and article metadata of four of its major conferences: the ACM conferences on Computer-Human Interaction (CHI), User Interface Software and Technology (UIST) andAdvanced Visual Interfaces (AVI) and the IEEE symposium on Information Visualization (InfoVis). This article describes many global and local patterns we discovered in this dataset, together with the exploration process that produced them. Some expected patterns emerged, such as that -like most social networks -co-authorship and citation networks exhibit a power-law degree distribution, with a few widely-collaborating authors and highly-cited articles. Also, the prestigious and long-established CHI conference has the highest impact (citations by the others). Unexpected insights included that the years when a given conference was most selective are not correlated with those that produced its most highly-referenced articles, and that influential authors have distinct patterns of collaboration.An interesting sidelight is that methods from the HCI field -exploratory data analysis by information visualization and direct-manipulation interaction -proved useful for this analysis. They allowed us to take an open-ended, exploratory approach, guided by the data itself. As we answered our original questions, new ones arose; as we confirmed patterns we expected, we discovered refinements, exceptions, and fascinating new ones.are the outliers? The great strength of exploratory analysis is its ability to raise unexpected questions. The drawback is that analysis can become a very drawn-out process, as the answer to one question raises many others that require further analysis. In this article, we describe our exploration process and provide a subset of interesting points for reflection, but we cannot hope to present a complete analysis of the field of human-computer interaction.This article is organized as follows: We present a discussion of related work, and then describe the process of dataset collection and cleaning, our approach to visual exploration, and how the visualizations were created. The central part of the article is the actual analysis, divided into three sections: an overview of the field describing important work, key researchers and the main topics across time for the four conferences; information about how articles reference each other and the patterns of citations between authors; and the collaboration networks that compare the community structure across conferences.

I n human-computer interaction, much of the literature on designing and evaluating colocated collaboration revolves around dedicated technology in the form of touch-sensitive displays, input devices, or software. Each of these has advantages for certain collaboration environments and situations. Adapting an application to colocated collaboration might appear to require using specialized hardware and reimplementing the application, for example, to ■ scale to specific presentation spaces such as large highresolution wall or tabletop displays, ■ employ head-mounted displays or CAVEs (Cave Automatic Virtual Environments), or ■ react to other forms of input such as direct touch, gloves, or pens. CoCoNutTrix extends the NodeTrix social-networkanalysis tool to enable multiuser interaction in collaborative environments. A user study verifies the low-cost retrofitting's effectiveness and highlights implications for practitioners.