Ji Soo Yi

TimeMatrix: Analyzing Temporal Social Networks Using Interactive Matrix-Based Visualizations

Visualization plays a crucial role in understanding dynamic social networks at many different levels (i.e., group, subgroup, and individual). Node-link-based visualization techniques are currently widely used for these tasks and have been demonstrated to be effective, but it was found that they also have limitations in representing temporal changes, particularly at the individual and subgroup levels. To overcome these limitations, this article presents a new network visualization technique, called “TimeMatrix,” based on a matrix representation. Interaction techniques, such as overlay controls, a temporal range slider, semantic zooming, and integrated network statistical measures, support analysts in studying temporal social networks. To validate the design, the article presents a user study involving three social scientists analyzing inter-organizational collaboration data. The study demonstrates how TimeMatrix may help analysts gain insights about the temporal aspects of network data that can be subsequently tested with network analytic methods.

Perception of Animated Node-Link Diagrams for Dynamic Graphs

Effective visualization of dynamic graphs remains an open research topic, and many state‐of‐the‐art tools use animated node‐link diagrams for this purpose. Despite its intuitiveness, the effectiveness of animation in node‐link diagrams has been questioned, and several empirical studies have shown that animation is not necessarily superior to static visualizations. However, the exact mechanics of perceiving animated node‐link diagrams are still unclear. In this paper, we study the impact of different dynamic graph metrics on user perception of the animation. After deriving candidate visual graph metrics, we perform an exploratory user study where participants are asked to reconstruct the event sequence in animated node‐link diagrams. Based on these findings, we conduct a second user study where we investigate the most important visual metrics in depth. Our findings show that node speed and target separation are prominent visual metrics to predict the performance of event sequencing tasks.

Visual Analytics for Multimodal Social Network Analysis: A Design Study with Social Scientists

Social network analysis (SNA) is becoming increasingly concerned not only with actors and their relations, but also with distinguishing between different types of such entities. For example, social scientists may want to investigate asymmetric relations in organizations with strict chains of command, or incorporate non-actors such as conferences and projects when analyzing coauthorship patterns. Multimodal social networks are those where actors and relations belong to different types, or modes, and multimodal social network analysis (mSNA) is accordingly SNA for such networks. In this paper, we present a design study that we conducted with several social scientist collaborators on how to support mSNA using visual analytics tools. Based on an openended, formative design process, we devised a visual representation called parallel node-link bands (PNLBs) that splits modes into separate bands and renders connections between adjacent ones, similar to the list view in Jigsaw. We then used the tool in a qualitative evaluation involving five social scientists whose feedback informed a second design phase that incorporated additional network metrics. Finally, we conducted a second qualitative evaluation with our social scientist collaborators that provided further insights on the utility of the PNLBs representation and the potential of visual analytics for mSNA.

How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking

In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvices information VIsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities: 1 encountering visualization, 2 constructing a frame, 3 exploring visualization, 4 questioning the frame, and 5 floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.

Direct manipulation through surrogate objects

Direct manipulation has had major influence on interface design since it was proposed by Shneiderman in 1982. Although directness generally benefits users, direct manipulation also has weaknesses. In some cases, such as when a user needs to manipulate small, attribute-rich objects or multiple objects simultaneously, indirect manipulation may be more efficient at the cost of directness or intuitiveness of the interaction. Several techniques have been developed over the years to address these issues, but these are all isolated and limited efforts with no coherent underlying principle. We propose the notion of Surrogate Interaction that ties together a large subset of these techniques through the use of a surrogate object that allow users to interact with the surrogate instead of the domain object. We believe that formalizing this family of interaction techniques will provide an additional and powerful interface design alternative for interaction designers, as well as uncover opportunities for future research.

How to filter out random clickers in a crowdsourcing-based study?

Crowdsourcing-based user studies have become increasingly popular in information visualization (InfoVis) and visual analytics (VA). However, it is still unclear how to deal with some undesired crowdsourcing workers, especially those who submit random responses simply to gain wages (random clickers, henceforth). In order to mitigate the impacts of random clickers, several studies simply exclude outliers, but this approach has a potential risk of losing data from participants whose performances are extreme even though they participated faithfully. In this paper, we evaluated the degree of randomness in responses from a crowdsourcing worker to infer whether the worker is a random clicker. Thus, we could reliably filter out random clickers and found that resulting data from crowdsourcing-based user studies were comparable with those of a controlled lab study. We also tested three representative reward schemes (piece-rate, quota, and punishment schemes) with four different levels of compensations (

Evaluating the Role of Time in Investigative Analysis of Document Collections

0.00,

Patterns for visualization evaluation

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A Comparative Study of Three Sorting Techniques in Performing Cognitive Tasks on a Tabular Representation

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Do People Really Experience Information Overload While Reading Online Reviews

4.00) on a crowdsourcing platform with a total of 1,500 crowdsourcing workers to investigate the influences that different payment conditions have on the number of random clickers. The results show that higher compensations decrease the proportion of random clickers, but such increase in participation quality cannot justify the associated additional costs. A detailed discussion on how to optimize the payment scheme and amount to obtain high-quality data economically is provided.