Abstract — Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks. Index Terms—Social networks, interactive graph visualization, attribute ranking, coordinated views, exploratory data analysis. 1

Although both statistical methods and visualizations have been used by network analysts, exploratory data analysis remains a challenge. We propose that a tight integration of these technologies in an interactive exploratory tool could dramatically speed insight development. To test the power of this integrated approach, we created a novel social network analysis tool, SocialAction, and conducted four long-term case studies with domain experts, each working on unique data sets with unique problems. The structured replicated case studies show that the integrated approach in SocialAction led to significant discoveries by a political analyst, a bibliometrician, a healthcare consultant, and a counter-terrorism researcher. Our contributions demonstrate that the tight integration of statistics and visualizations improves exploratory data analysis, and that our evaluation methodology for long-term case studies captures the research strategies of data analysts.

We present LiveRAC, a visualization system that supports the analysis of large collections of system management timeseries data consisting of hundreds of parameters across thousands of network devices. LiveRAC provides high information density using a reorderable matrix of charts, with semantic zooming adapting each chart’s visual representation to the available space. LiveRAC allows side-by-side visual comparison of arbitrary groupings of devices and parameters at multiple levels of detail. A staged design and development process culminated in the deployment of LiveRAC in a production environment. We conducted an informal longitudinal evaluation of LiveRAC to better understand which proposed visualization techniques were most useful in the target environment.

We introduce grounded evaluation as a process that attempts to ensure that the evaluation of an information visualization tool is situated within the context of its intended use. We discuss the process and scope of grounded evaluation in general, and then describe how qualitative inquiry may be a beneficial approach as part of this process. We advocate for increased attention to the field of qualitative inquiry early in the information visualization development life cycle, as it tries to achieve a richer understanding by using a more holistic approach considering the interplay between factors that influence visualizations, their development, and their use. We present three case studies in which we successfully used observational techniques to inform our understanding of the visual analytics process in groups, medical diagnostic reasoning, and visualization use among computational linguists.

During exploratory data analysis, visualizations are often useful for making sense of complex data sets. However, as data sets increase in size and complexity, static information visualizations decrease in comprehensibility. Interactive techniques can yield valuable discoveries, but current data analysis tools typically support only opportunistic exploration that may be inefficient and incomplete. We present a refined architecture that uses systematic yet flexible (SYF) design goals to guide domain expert users through complex exploration of data over days, weeks and months. The SYF system aims to support exploratory data analysis with some of the simplicity of an e-commerce check-out while providing added flexibility to pursue insights. The SYF system provides an overview of the analysis process, suggests unexplored states, allows users to annotate useful states, supports collaboration, and enables reuse of successful strategies. The affordances of the SYF system are demonstrated by integrating it into a social network analysis tool employed by social scientists and intelligence analysts. The SYF system is a tool-independent component and can be incorporated into other data analysis tools.

TRECVID participants have enjoyed consistent success using storyboard interfaces for shot-based retrieval, as measured by TRECVID interactive search mean average precision (MAP). However, much is lost by only looking at MAP, and especially by neglecting to bring in representatives of the target user communities to conduct such tasks. This paper reports on the use of within-subjects experiments to reduce subject variability and emphasize the examination of specific video search interface features for their effectiveness in interactive retrieval and user satisfaction. A series of experiments is surveyed to illustrate the gradual realization of getting non-experts to utilize non-textual query features through interface adjustments. Notably, the paper explores the use of the search system by government intelligence analysts, concluding that a variety of search methods are useful for news video retrieval and lead to improved satisfaction. This community, dominated by text search system expertise but still new to video and image search, performed better with and favored a system with image and concept query capabilities over an exclusive text-search system. The user study also found that sports topics mean nothing for this user community and tens of relevant shots collected into the answer set are considered enough to satisfy the information need. Lessons learned from these user interactions are reported, with recommendations on both interface improvements for video retrieval systems and enhancing the ecological validity of video retrieval interface evaluations.

Even though “providing insight ” has been considered one of the main purposes of information visualization (InfoVis), we feel that insight is still a not-well-understood concept in this context. Inspired by research in sensemaking, we realized the importance of the procedural aspects in understanding insight. Thus, rather than asking “What is insight? ” we instead focus on “How do people gain insights? ” In an effort to better understand and characterize insight, we reviewed previous literature in InfoVis, seeking other researchers ’ comments and views on this concept. We found that: 1) Insights are often regarded as end results of using InfoVis and the procedures to gain insight have been largely veiled; 2) Four largely distinctive processes of gaining insight (Provide Overview, Adjust, Detect Pattern, and Match Mental Model) have been discussed in the InfoVis literature; and 3) These different processes provide some hints to understand the procedures in which insight can be gained from InfoVis. We hope that our findings help researchers and practitioners evaluate InfoVis systems and technologies in a more insight-oriented way. Categories and Subject Descriptors

Abstract—Even though information visualization (InfoVis) research has matured in recent years, it is generally acknowledged that the field still lacks supporting, encompassing theories. In this paper, we argue that the distributed cognition framework can be used to substantiate the theoretical foundation of InfoVis. We highlight fundamental assumptions and theoretical constructs of the distributed cognition approach, based on the cognitive science literature and a real life scenario. We then discuss how the distributed cognition framework can have an impact on the research directions and methodologies we take as InfoVis researchers. Our contributions are as follows. First, we highlight the view that cognition is more an emergent property of interaction than a property of the human mind. Second, we argue that a reductionist approach to study the abstract properties of isolated human minds may not be useful in informing InfoVis design. Finally we propose to make cognition an explicit research agenda, and discuss the implications on how we perform evaluation and theory building. Index Terms—Information visualization, distributed cognition, interaction, representation, theory and methods 1

guidelines for categorized overviews

Abstract: Gaining a rapid overview of an emerging scientific topic, sometimes called research fronts, is an increasingly common task due to the growing amount of interdisciplinary collaboration occurring across fields. Visual overviews that show temporal patterns of paper publication and citation links among papers can help researchers and analysts to see the rate of growth of topics, identify key papers, and understand influences across sub-disciplines. This paper applies a novel network visualization tool based on meaningful layouts of nodes to present research fronts and show citation links that indicate influences across research fronts. The twodimensional layouts with multiple regions and user control of link visibility enable rapid exploration. We assess the value of our tool for this application by conducting a case study with six domain experts over a four-month period. The main benefits were being able (1) to easily identify key papers and see the increasing number of papers within a research front and (2) to quickly see the strength and direction of influence across related research fronts.