DevLan/vispubs huggingface dataset:
Perceptual Organization in User-Generated Graph Layouts
Authors: Frank van Ham, Bernice E. Rogowitz
Published at InfoVis, 2008

Many graph layout algorithms optimize visual characteristics to achieve useful representations. Implicitly, their goal is to create visual representations that are more intuitive to human observers. In this paper, we asked users to explicitly manipulate nodes in a network diagram to create layouts that they felt best captured the relationships in the data. This allowed us to measure organizational behavior directly, allowing us to evaluate the perceptual importance of particular visual features, such as edge crossings and edge-lengths uniformity. We also manipulated the interior structure of the node relationships by designing data sets that contained clusters, that is, sets of nodes that are strongly interconnected. By varying the degree to which these clusters were ldquomaskedrdquo by extraneous edges we were able to measure observerspsila sensitivity to the existence of clusters and how they revealed them in the network diagram. Based on these measurements we found that observers are able to recover cluster structure, that the distance between clusters is inversely related to the strength of the clustering, and that users exhibit the tendency to use edges to visually delineate perceptual groups. These results demonstrate the role of perceptual organization in representing graph data and provide concrete recommendations for graph layout algorithms.
Rapid Graph Layout Using Space filling Curves
Authors: Chris Muelder, Kwan-Liu Ma
Published at InfoVis, 2008

Network data frequently arises in a wide variety of fields, and node-link diagrams are a very natural and intuitive representation of such data. In order for a node-link diagram to be effective, the nodes must be arranged well on the screen. While many graph layout algorithms exist for this purpose, they often have limitations such as high computational complexity or node colocation. This paper proposes a new approach to graph layout through the use of space filling curves which is very fast and guarantees that there will be no nodes that are colocated. The resulting layout is also aesthetic and satisfies several criteria for graph layout effectiveness.
Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation
Authors: Niklas Elmqvist, Pierre Dragicevic, Jean-Daniel Fekete
Published at InfoVis, 2008

Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using scatterplots. This exploration is performed using a matrix of scatterplots that gives an overview of the possible configurations, thumbnails of the scatterplots, and support for interactive navigation in the multidimensional space. Transitions between scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets.
Spatially Ordered Treemaps
Authors: Jo Wood, Jason Dykes
Published at InfoVis, 2008

Existing treemap layout algorithms suffer to some extent from poor or inconsistent mappings between data order and visual ordering in their representation, reducing their cognitive plausibility. While attempts have been made to quantify this mismatch, and algorithms proposed to minimize inconsistency, solutions provided tend to concentrate on one-dimensional ordering. We propose extensions to the existing squarified layout algorithm that exploit the two-dimensional arrangement of treemap nodes more effectively. Our proposed spatial squarified layout algorithm provides a more consistent arrangement of nodes while maintaining low aspect ratios. It is suitable for the arrangement of data with a geographic component and can be used to create tessellated cartograms for geovisualization. Locational consistency is measured and visualized and a number of layout algorithms are compared. CIELab color space and displacement vector overlays are used to assess and emphasize the spatial layout of treemap nodes. A case study involving locations of tagged photographs in the Flickr database is described.
Stacked Graphs - Geometry & Aesthetics
Authors: Lee Byron, Martin Wattenberg
Published at InfoVis, 2008

In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility.
The Shaping of Information by Visual Metaphors
Authors: Caroline Ziemkiewicz, Robert Kosara
Published at InfoVis, 2008

The nature of an information visualization can be considered to lie in the visual metaphors it uses to structure information. The process of understanding a visualization therefore involves an interaction between these external visual metaphors and the user's internal knowledge representations. To investigate this claim, we conducted an experiment to test the effects of visual metaphor and verbal metaphor on the understanding of tree visualizations. Participants answered simple data comprehension questions while viewing either a treemap or a node-link diagram. Questions were worded to reflect a verbal metaphor that was either compatible or incompatible with the visualization a participant was using. The results suggest that the visual metaphor indeed affects how a user derives information from a visualization. Additionally, we found that the degree to which a user is affected by the metaphor is strongly correlated with the user's ability to answer task questions correctly. These findings are a first step towards illuminating how visual metaphors shape user understanding, and have significant implications for the evaluation, application, and theory of visualization.
The Word Tree, an Interactive Visual Concordance
Authors: Martin Wattenberg, Fernanda B. Viégas
Published at InfoVis, 2008

We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.
VisGets: Coordinated Visualizations for Web-based Information Exploration and Discovery
Authors: Marian Dörk, Sheelagh Carpendale, Christopher Collins 0001, Carey L. Williamson
Published at InfoVis, 2008

In common Web-based search interfaces, it can be difficult to formulate queries that simultaneously combine temporal, spatial, and topical data filters. We investigate how coordinated visualizations can enhance search and exploration of information on the World Wide Web by easing the formulation of these types of queries. Drawing from visual information seeking and exploratory search, we introduce VisGets - interactive query visualizations of Web-based information that operate with online information within a Web browser. VisGets provide the information seeker with visual overviews of Web resources and offer a way to visually filter the data. Our goal is to facilitate the construction of dynamic search queries that combine filters from more than one data dimension. We present a prototype information exploration system featuring three linked VisGets (temporal, spatial, and topical), and used it to visually explore news items from online RSS feeds.
Vispedia: Interactive Visual Exploration of Wikipedia Data via Search-Based Integration
Authors: Bryan Chan 0001, Leslie Wu, Justin Talbot, Mike Cammarano, Pat Hanrahan
Published at InfoVis, 2008

Wikipedia is an example of the collaborative, semi-structured data sets emerging on the Web. These data sets have large, non-uniform schema that require costly data integration into structured tables before visualization can begin. We present Vispedia, a Web-based visualization system that reduces the cost of this data integration. Users can browse Wikipedia, select an interesting data table, then use a search interface to discover, integrate, and visualize additional columns of data drawn from multiple Wikipedia articles. This interaction is supported by a fast path search algorithm over DBpedia, a semantic graph extracted from Wikipedia's hyperlink structure. Vispedia can also export the augmented data tables produced for use in traditional visualization systems. We believe that these techniques begin to address the "long tail" of visualization by allowing a wider audience to visualize a broader class of data. We evaluated this system in a first-use formative lab study. Study participants were able to quickly create effective visualizations for a diverse set of domains, performing data integration as needed.
Visualizing Incomplete and Partially Ranked Data
Authors: Paul Kidwell, Guy Lebanon, William S. Cleveland
Published at InfoVis, 2008

Ranking data, which result from m raters ranking n items, are difficult to visualize due to their discrete algebraic structure, and the computational difficulties associated with them when n is large. This problem becomes worse when raters provide tied rankings or not all items are ranked. We develop an approach for the visualization of ranking data for large n which is intuitive, easy to use, and computationally efficient. The approach overcomes the structural and computational difficulties by utilizing a natural measure of dissimilarity for raters, and projecting the raters into a low dimensional vector space where they are viewed. The visualization techniques are demonstrated using voting data, jokes, and movie preferences.
Viz-A-Vis: Toward Visualizing Video through Computer Vision
Authors: Mario Romero, Jay Summet, John T. Stasko, Gregory D. Abowd
Published at InfoVis, 2008

In the established procedural model of information visualization, the first operation is to transform raw data into data tables. The transforms typically include abstractions that aggregate and segment relevant data and are usually defined by a human, user or programmer. The theme of this paper is that for video, data transforms should be supported by low level computer vision. High level reasoning still resides in the human analyst, while part of the low level perception is handled by the computer. To illustrate this approach, we present Viz-A-Vis, an overhead video capture and access system for activity analysis in natural settings over variable periods of time. Overhead video provides rich opportunities for long-term behavioral and occupancy analysis, but it poses considerable challenges. We present initial steps addressing two challenges. First, overhead video generates overwhelmingly large volumes of video impractical to analyze manually. Second, automatic video analysis remains an open problem for computer vision.
Who Votes For What? A Visual Query Language for Opinion Data
Authors: Geoffrey M. Draper, Richard F. Riesenfeld
Published at InfoVis, 2008

Surveys and opinion polls are extremely popular in the media, especially in the months preceding a general election. However, the available tools for analyzing poll results often require specialized training. Hence, data analysis remains out of reach for many casual computer users. Moreover, the visualizations used to communicate the results of surveys are typically limited to traditional statistical graphics like bar graphs and pie charts, both of which are fundamentally noninteractive. We present a simple interactive visualization that allows users to construct queries on large tabular data sets, and view the results in real time. The results of two separate user studies suggest that our interface lowers the learning curve for naive users, while still providing enough analytical power to discover interesting correlations in the data.
A Four-level Focus+Context Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data
Authors: Philipp Muigg, Johannes Kehrer, Steffen Oeltze, Harald Piringer, Helmut Doleisch, Bernhard Preim, Helwig Hauser
Published at EuroVis, 2008

In this paper we present a new approach to the interactive visual analysis of time‐dependent scientific data – both from measurements as well as from computational simulation – by visualizing a scalar function over time for each of tenthousands or even millions of sample points. In order to cope with overdrawing and cluttering, we introduce a new four‐level method of focus+context visualization. Based on a setting of coordinated, multiple views (with linking and brushing), we integrate three different kinds of focus and also the context in every single view. Per data item we use three values (from the unit interval each) to represent to which degree the data item is part of the respective focus level. We present a color compositing scheme which is capable of expressing all three values in a meaningful way, taking semantics and their relations amongst each other (in the context of our multiple linked view setup) into account. Furthermore, we present additional image‐based postprocessing methods to enhance the visualization of large sets of function graphs, including a texture‐based technique based on line integral convolution (LIC). We also propose advanced brushing techniques which are specific to the time‐dependent nature of the data (in order to brush patterns over time more efficiently). We demonstrate the usefulness of the new approach in the context of medical perfusion data.
A Screen Space Quality Method for Data Abstraction
Authors: Jimmy Johansson 0001, Matthew Cooper 0001
Published at EuroVis, 2008

The rendering of large data sets can result in cluttered displays and non‐interactive update rates, leading to time consuming analyses. A straightforward solution is to reduce the number of items, thereby producing an abstraction of the data set. For the visual analysis to remain accurate, the graphical representation of the abstraction must preserve the significant features present in the original data. This paper presents a screen space quality method, based on distance transforms, that measures the visual quality of a data abstraction. This screen space measure is shown to better capture significant visual structures in data, compared with data space measures. The presented method is implemented on the GPU, allowing interactive creation of high quality graphical representations of multivariate data sets containing tens of thousands of items.
Abstractive Representation and Exploration of Hierarchically Clustered Diffusion Tensor Fiber Tracts
Authors: Wei Chen 0001, Song Zhang 0004, Stephen Correia, David S. Ebert
Published at EuroVis, 2008

Diffusion tensor imaging (DTI) has been used to generate fibrous structures in both brain white matter and muscles. Fiber clustering groups the DTI fibers into spatially and anatomically related tracts. As an increasing number of fiber clustering methods have been recently developed, it is important to display, compare, and explore the clustering results efficiently and effectively. In this paper, we present an anatomical visualization technique that reduces the geometric complexity of the fiber tracts and emphasizes the high‐level structures. Beginning with a volumetric diffusion tensor image, we first construct a hierarchical clustering representation of the fiber bundles. These bundles are then reformulated into a 3D multi‐valued volume data. We then build a set of geometric hulls and principal fibers to approximate the shape and orientation of each fiber bundle. By simultaneously visualizing the geometric hulls, individual fibers, and other data sets such as fractional anisotropy, the overall shape of the fiber tracts are highlighted, while preserving the fibrous details. A rater with expert knowledge of white matter structure has evaluated the resulting interactive illustration and confirmed the improvement over straightforward DTI fiber tract visualization.
Animating Causal Overlays
Authors: Lyn Bartram, Miao Yao
Published at EuroVis, 2008

Most approaches to representing causality, such as the common causal graph, require a separate and static view, but in many cases it is useful to add the dimension of causality to the context of an existing visualization. Building on research from perceptual psychology that shows the perception of causality is a low‐level visual event derived from certain types of motion, we are investigating how to add animated causal representations, called visual causal vectors, onto other visualizations. We refer to these as causal overlays. Our initial experimental results show this approach has great potential but that extra cues are needed to elicit the perception of causality when the motions are overlaid on other graphical objects. In this paper we describe the approach and report on a study that examined two issues of this technique: how to accurately convey the causal flow and how to represent the strength of the causal effect.
Automatic Detection and Visualization of Distinctive Structures in 3D Unsteady Multi-fields
Authors: Heike Jänicke, Michael Böttinger, Xavier Tricoche, Gerik Scheuermann
Published at EuroVis, 2008

Current unsteady multi‐field simulation data‐sets consist of millions of data‐points. To efficiently reduce this enormous amount of information, local statistical complexity was recently introduced as a method that identifies distinctive structures using concepts from information theory. Due to high computational costs this method was so far limited to 2D data. In this paper we propose a new strategy for the computation that is substantially faster and allows for a more precise analysis. The bottleneck of the original method is the division of spatio‐temporal configurations in the field (light‐cones) into different classes of behavior. The new algorithm uses a density‐driven Voronoi tessellation for this task that more accurately captures the distribution of configurations in the sparsely sampled high‐dimensional space. The efficient computation is achieved using structures and algorithms from graph theory. The ability of the method to detect distinctive regions in 3D is illustrated using flow and weather simulations.
Centrality Based Visualization of Small World Graphs
Authors: Frank van Ham, Martin Wattenberg
Published at EuroVis, 2008

Current graph drawing algorithms enable the creation of two dimensional node‐link diagrams of huge graphs. However, for graphs with low diameter (of which “small world” graphs are a subset) these techniques begin to break down visually even when the graph has only a few hundred nodes. Typical algorithms produce images where nodes clump together in the center of the screen, making it hard to discern structure and follow paths. This paper describes a solution to this problem, which uses a global edge metric to determine a subset of edges that capture the graph's intrinsic clustering structure. This structure is then used to create an embedding of the graph, after which the remaining edges are added back in. We demonstrate applications of this technique to a number of real world examples.
Classification and Uncertainty Visualization of Dendritic Spines from Optical Microscopy Imaging
Authors: Firdaus Janoos, Boonthanome Nouanesengsy, Xiaoyin Xu, Raghu Machiraju, Stephen T. C. Wong
Published at EuroVis, 2008

Neuronal dendrites and their spines affect the connectivity of neural networks, and play a significant role in many neurological conditions. Neuronal function is observed to be closely correlated with the appearance, disappearance and morphology of the spines. Automatic 3‐D reconstruction of neurons from light microscopy images, followed by the identification, classification and visualization of dendritic spines is therefore essential for studying neuronal physiology and biophysical properties. In this paper, we present a method to reconstruct dendrites using a surface representation of the dendrite. The 1‐D skeleton of the dendritic surface is then extracted by a medial geodesic function that is robust and topologically correct. This is followed by a Bayesian identification and classification of the spines. The dendrite and spines are visualized in a manner that displays the spines' types and the inherent uncertainty in identification and classification. We also describe a user study conducted to validate the accuracy of the classification and the efficacy of the visualization.
Code Flows: Visualizing Structural Evolution of Source Code
Authors: Alexandru C. Telea, David Auber
Published at EuroVis, 2008

Understanding detailed changes done to source code is of great importance in software maintenance. We present Code Flows, a method to visualize the evolution of source code geared to the understanding of fine and mid‐level scale changes across several file versions. We enhance an existing visual metaphor to depict software structure changes with techniques that emphasize both following unchanged code as well as detecting and highlighting important events such as code drift, splits, merges, insertions and deletions. The method is illustrated with the analysis of a real‐world C++ code system.
Computing Local Signed Distance Fields for Large Polygonal Models
Authors: Byungjoon Chang, Deukhyun Cha, Insung Ihm
Published at EuroVis, 2008

The signed distance field for a polygonal model is a useful representation that facilitates efficient computation in many visualization and geometric processing tasks. Often it is more effective to build a local distance field only within a narrow band around the surface that holds local geometric information for the model. In this paper, we present a novel technique to construct a volumetric local signed distance field of a polygonal model. To compute the local field efficiently, exactly those cells that cross the polygonal surface are found first through a new voxelization method, building a list of intersecting triangles for each boundary cell. After their neighboring cells are classified, the triangle lists are exploited to compute the local signed distance field with minimized voxel‐to‐triangle distance computations. While several efficient methods for computing the distance field, particularly those harnessing the graphics processing unit's (GPU's) processing power, have recently been proposed, we focus on a CPU‐based technique, intended to deal flexibly with large polygonal models and high‐resolution grids that are often too bulky for GPU computation.