Bahador Saket

Node, Node-Link, and Node-Link-Group Diagrams: An Evaluation.

Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting, the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams, node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain snecific visualizations tools.

Beyond Usability and Performance: A Review of User Experience-focused Evaluations in Visualization

Traditionally, studies of data visualization techniques and systems have evaluated visualizations with respect to usability goals such as effectiveness and efficiency. These studies assess performance-related metrics such as time and correctness of participants completing analytic tasks. Alternatively, several studies in InfoVis recently have evaluated visualizations by investigating user experience goals such as memorability, engagement, enjoyment and fun. These studies employ somewhat different evaluation methodologies to assess these other goals. The growing number of these studies, their alternative methodologies, and disagreements concerning their importance have motivated us to more carefully examine them. In this article, we review this growing collection of visualization evaluations that examine user experience goals and we discuss multiple issues regarding the studies including questions about their motivation and utility. Our aim is to provide a resource for future work that plans to evaluate visualizations using these goals.

Group-Level Graph Visualization Taxonomy

Task taxonomies for graph and network visualization focus on tasks commonly encountered when analyzing graph connectivity and topology. However, in many application fields such as the social sciences (social networks), biology (protein interaction models), software engineering (program call graphs), connectivity and topology information is intertwined with grouping and clustering information. Several recent visualization techniques, such as BubbleSets, LineSets and GMap, make explicit use of grouping and clustering, but evaluating such visualizations has been difficult due to the lack of standardized group-level tasks. With this in mind, our goal is to define a new set of tasks that assess group-level comprehension. We propose several types of group-level tasks and provide several examples of each type. Finally, we characterize some of the proposed tasks using a multi-level typology of

Are Crossings Important for Drawing Large Graphs

Reducing the number of edge crossings is considered one of the most important graph drawing aesthetics. While real-world graphs tend to be large and dense, most of the earlier work on evaluating the impact of edge crossings utilizes relatively small graphs that are manually generated and manipulated. We study the effect on task performance of increased edge crossings in automatically generated layouts for graphs, from different datasets, with different sizes, and with different densities. The results indicate that increasing the number of crossings negatively impacts accuracy and performance time and that impact is significant for small graphs but not significant for large graphs. We also quantitatively evaluate the impact of edge crossings on crossing angles and stress in automatically constructed graph layouts. We find a moderate correlation between minimizing stress and the minimizing the number of crossings.

Map-based Visualizations Increase Recall Accuracy of Data

We investigate the memorability of data represented in two different visualization designs. In contrast to recent studies that examine which types of visual information make visualizations memorable, we examine the effect of different visualizations on time and accuracy of recall of the displayed data, minutes and days after interaction with the visualizations. In particular, we describe the results of an evaluation comparing the memorability of two different visualizations of the same relational data: node‐link diagrams and map‐based visualization. We find significant differences in the accuracy of the tasks performed, and these differences persist days after the original exposure to the visualizations. Specifically, participants in the study recalled the data better when exposed to map‐based visualizations as opposed to node‐link diagrams. We discuss the scope of the study and its limitations, possible implications, and future directions.

Visualization by Demonstration: An Interaction Paradigm for Visual Data Exploration

Although data visualization tools continue to improve, during the data exploration process many of them require users to manually specify visualization techniques, mappings, and parameters. In response, we present the Visualization by Demonstration paradigm, a novel interaction method for visual data exploration. A system which adopts this paradigm allows users to provide visual demonstrations of incremental changes to the visual representation. The system then recommends potential transformations (Visual Representation, Data Mapping, Axes, and View Specification transformations) from the given demonstrations. The user and the system continue to collaborate, incrementally producing more demonstrations and refining the transformations, until the most effective possible visualization is created. As a proof of concept, we present VisExemplar, a mixed-initiative prototype that allows users to explore their data by recommending appropriate transformations in response to the given demonstrations.

Comparing node-link and node-link-group visualizations from an enjoyment perspective

While evaluation studies in visualization often involve traditional performance measurements, there has been a concerted effort to move beyond time and accuracy. Of these alternative aspects, memorability and recall of visualizations have been recently considered, but other aspects such as enjoyment and engagement are not as well explored. We study the enjoyment of two different visualization methods through a user study. In particular, we describe the results of a three‐phase experiment comparing the enjoyment of two different visualizations of the same relational data: node‐link and node‐link‐group visualizations. The results indicate that the participants in this study found node‐link‐group visualizations more enjoyable than node‐link visualizations.

Towards Understanding Enjoyment and Flow in Information Visualization

Traditionally, evaluation studies in information visualization have measured effectiveness by assessing performance time and accuracy. More recently, there has been a concerted effort to understand aspects beyond time and errors. In this paper we study enjoyment, which, while arguably not the primary goal of visualization, has been shown to impact performance and memorability. Different models of enjoyment have been proposed in psychology, education and gaming; yet there is no standard approach to evaluate and measure enjoyment in visualization. In this paper we relate the flow model of Csikszentmihalyi to Munzners nested model of visualization evaluation and previous work in the area. We suggest that, even though previous papers tackled individual elements of flow, in order to understand what specifically makes a visualization enjoyable, it might be necessary to measure all specific elements.

Traceband: locating missing items by visual remembrance

Finding missing items has always been troublesome. To tackle the hassle, several systems have been suggested; yet they are inflexible due to excessive setup time, operational cost, and effectiveness. We present Traceband; a lightweight and portable bracelet, which keeps track of every targeted commonly used object that a user interacts with. Users can find the location of missing items via a web-based software portal.