ConVis: A Visual Text Analytic System for Exploring Blog Conversations

Today it is quite common for people to exchange hundreds of comments in online conversations (e.g., blogs). Often, it can be very difficult to analyze and gain insights from such long conversations. To address this problem, we present a visual text analytic system that tightly integrates interactive visualization with novel text mining and summarization techniques to fulfill information needs of users in exploring conversations. At first, we perform a user requirement analysis for the domain of blog conversations to derive a set of design principles. Following these principles, we present an interface that visualizes a combination of various metadata and textual analysis results, supporting the user to interactively explore the blog conversations. We conclude with an informal user evaluation, which provides anecdotal evidence about the effectiveness of our system and directions for further design.     

Papilio: Visualizing Android Application Permissions

We introduce Papilio, a new visualization technique for visualizing permissions of real‐world Android applications. We explore the development of layouts that exploit the directed acyclic nature of Android application permission data to develop a new explicit layout technique that incorporates aspects of set membership, node‐link diagrams and matrix layouts. By grouping applications based on sets of requested permissions, a structure can be formed with partially ordered relations. The Papilio layout shows sets of applications centrally, the relations among applications on one side and application permissions, as the reason behind the existence of the partial order, on the other side. Using Papilio to explore a set of Android applications as a case study has led to new security findings regarding permission usage by Android applications.     

Exploratory Visual Analysis for Animal Movement Ecology

Movement ecologists study animals' movement to help understand their behaviours and interactions with each other and the environment. Data from GPS loggers are increasingly important for this. These data need to be processed, segmented and summarised for further visual and statistical analysis, often using predefined parameters. Usually, this process is separate from the subsequent visual and statistical analysis, making it difficult for these results to inform the data processing and to help set appropriate scale and thresholds parameters. This paper explores the use of highly interactive visual analytics techniques to close the gap between processing raw data and exploratory visual analysis. Working closely with animal movement ecologists, we produced requirements to enable data characteristics to be determined, initial research questions to be investigated, and the suitability of data for further analysis to be assessed. We design visual encodings and interactions to meet these requirements and provide software that implements them. We demonstrate these techniques with indicative research questions for a number of bird species, provide software, and discuss wider implications for animal movement ecology.     

Structural Analysis of Multivariate Point Clouds Using Simplicial Chains

Topological and geometrical methods constitute common tools for the analysis of high‐dimensional scientific data sets. Geometrical methods such as projection algorithms focus on preserving distances in the data set. Topological methods such as contour trees, by contrast, focus on preserving structural and connectivity information. By combining both types of methods, we want to benefit from their individual advantages. To this end, we describe an algorithm that uses persistent homology to analyse the topology of a data set. Persistent homology identifies high‐dimensional holes in data sets, describing them as simplicial chains. We localize these chains using geometrical information of the data set, which we obtain from geodesic distances on a neighbourhood graph. The localized chains describe the structure of point clouds. We represent them using an interactive graph, in which each node describes a single chain and its geometrical properties. This graph yields a more intuitive understanding of multivariate point clouds and simplifies comparisons of time‐varying data. Our method focuses on detecting and analysing inhomogeneous regions, i.e. holes, in a data set because these regions characterize data in a different manner, thereby leading to new insights. We demonstrate the potential of our method on data sets from particle physics, political science and meteorology.     

Using Visualization to Explore Original and Anonymized LBSN Data

We present GSUVis, a visualization tool designed to provide better understanding of location‐based social network (LBSN) data. LBSN data is one of the most important sources of information for transportation, marketing, health, and public safety. LBSN data consumers are interested in accessing and analysing data that is as complete and as accurate as possible. However, LBSN data contains sensitive information about individuals. Consequently, data anonymization is of critical importance if this data is to be made available to consumers. However, anonymization commonly reduces the utility of information available. Working with privacy experts, we designed GSUVis a visual analytic tool to help experts better understand the effects of anonymization techniques on LBSN data utility. One of GSUVis's primary goals is to make it possible for people to use LBSN data, without requiring them to gain deep knowledge about data anonymization. To inform the design of GSUVis, we interviewed privacy experts, and collected their tasks and system requirements. Based on this understanding, we designed and implemented GSUVis. It applies two anonymization algorithms for social and location trajectory data to a real‐world LBSN dataset and visualizes the data both before and after anonymization. Through feedback from domain experts, we reflect on the effectiveness of GSUVis and the impact of anonymization using visualization.     

TimeRadarTrees: Visualizing Dynamic Compound Digraphs

The evolution of dependencies in information hierarchies can be modeled by sequences of compound digraphs with edge weights. In this paper we present a novel approach to visualize such sequences of graphs. It uses radial tree layout to draw the hierarchy, and circle sectors to represent the temporal change of edges in the digraphs. We have developed several interaction techniques that allow the users to explore the structural and temporal data. Smooth animations help them to track the transitions between views. The usefulness of the approach is illustrated by examples from very different application domains.     

Density Displays for Data Stream Monitoring

In many business applications, large data workloads such as sales figures or process performance measures need to be monitored in real‐time. The data analysts want to catch problems in flight to reveal the root cause of anomalies. Immediate actions need to be taken before the problems become too expensive or consume too many resources. In the meantime, analysts need to have the “big picture” of what the information is about. In this paper, we derive and analyze two real‐time visualization techniques for managing density displays: (1) circular overlay d isplays which visualize large volumes of data without data shift movements after the display is full, thus freeing the analyst from adjusting the mental picture of the data after each data shift; and (2) variable resolution density displays which allow users to get the entire view without cluttering. We evaluate these techniques with respect to a number of evaluation measures, such as constancy of the display and usage of display space, and compare them to conventional d isplays with periodic shifts. Our real time data monitoring system also provides advanced interactions such as a local root cause analysis for further exploration. The applications using a number of real‐world data sets show the wide applicability and usefulness of our ideas.     

The Undistort Lens

Detail‐in‐context lens techniques can be useful for exploring visualizations of data spaces that are too large or have too much detail to fit in regular displays. For example, by bending the space in the right way we can bring together details from two separate areas for easy comparison while roughly keeping the context that situates each area within the global space. While these techniques can be powerful tools, they also introduce distortions that need to be understood, and often the tools have to be disabled in order to have access to the undistorted data. We introduce the undistort lens, a complement to existing distortion‐based techniques that provides a local and separate presentation of the original geometry without affecting any distortion‐based lenses currently used in the presentation. The undistort lens is designed to allow interactive access to the underlying undistorted data within the context of the distorted space, and to enable a better understanding of the distortions. The paper describes the implementation of a generic back‐mapping mechanism that enables the implementation of undistort lenses for arbitrary distortion based techniques, including those presented in the lens literature. We also provide a series of use‐case scenarios that demonstrate the situations in which the technique can complement existing lenses.     

Graffinity: Visualizing Connectivity in Large Graphs

Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node‐link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query‐based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open‐source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open‐source tool with illustrative examples using flight and connectomics data.     

Visualizing the Evolution of Compound Digraphs with TimeArcTrees

Compound digraphs are a widely used model in computer science. In many application domains these models evolve over time. Only few approaches to visualize such dynamic compound digraphs exist and mostly use animation to show the dynamics. In this paper we present a new visualization tool called TimeArcTrees that visualizes weighted, dynamic compound digraphs by drawing a sequence of node-link diagrams in a single view. Compactness is achieved by aligning the nodes of a graph vertically. Edge crossings are reduced by drawing upward and downward edges separately as colored arcs. Horizontal alignment of the instances of the same node in different graphs facilitates comparison of the graphs in the sequence. Many interaction techniques allow to explore the given graphs. Smooth animation supports the user to better track the transitions between views and to preserve his or her mental map. We illustrate the usefulness of the tool by looking at the particular problem of how shortest paths evolve over time. To this end, we applied the system to an evolving graph representing the German Autobahn and its traffic jams.     

Visual Analysis of Large Graphs: State‐of‐the‐Art and Future Research Challenges

The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques cover techniques that had been introduced until 2000 or concentrate only on graph layouts published until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time‐varying graphs. Also, in accordance with ever growing amounts of graph‐structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State‐of‐the‐Art Report, we survey available techniques for the visual analysis of large graphs. Our review first considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process. We also present main open research challenges in this field.     

Refinery: Visual Exploration of Large,Heterogeneous Networks through Associative Browsing

Browsing is a fundamental aspect of exploratory information‐seeking. Associative browsing represents a common and intuitive set of exploratory strategies in which users step iteratively from familiar to novel bits of information. In this paper, we examine associative browsing as a strategy for bottom‐up exploration of large, heterogeneous networks. We present Refinery, an interactive visualization system informed by guidelines for associative browsing drawn from literature on exploratory information‐seeking. These guidelines motivate Refinery's query model, which allows users to simply and expressively construct queries using heterogeneous sets of nodes. This system computes degree‐of‐interest scores for associated content using a fast, random‐walk algorithm. Refinery visualizes query nodes within a subgraph of results, providing explanatory context, facilitating serendipitous discovery, and stimulating continued exploration. A study of 12 academic researchers using Refinery to browse publication data demonstrates how the system enables discovery of valuable new content, even within existing areas of expertise.     

SmallWorlds: Visualizing Social Recommendations

We present SmallWorlds, a visual interactive graph‐based interface that allows users to specify, refine and build item‐preference profiles in a variety of domains. The interface facilitates expressions of taste through simple graph interactions and these preferences are used to compute personalized, fully transparent item recommendations for a target user. Predictions are based on a collaborative analysis of preference data from a user's direct peer group on a social network. We find that in addition to receiving transparent and accurate item recommendations, users also learn a wealth of information about the preferences of their peers through interaction with our visualization. Such information is not easily discoverable in traditional text based interfaces. A detailed analysis of our design choices for visual layout, interaction and prediction techniques is presented. Our evaluations discuss results from a user study in which SmallWorlds was deployed as an interactive recommender system on Facebook.     

Pathfinder: Visual Analysis of Paths in Graphs

The analysis of paths in graphs is highly relevant in many domains. Typically, path‐related tasks are performed in node‐link layouts. Unfortunately, graph layouts often do not scale to the size of many real world networks. Also, many networks are multivariate, i.e., contain rich attribute sets associated with the nodes and edges. These attributes are often critical in judging paths, but directly visualizing attributes in a graph layout exacerbates the scalability problem. In this paper, we present visual analysis solutions dedicated to path‐related tasks in large and highly multivariate graphs. We show that by focusing on paths, we can address the scalability problem of multivariate graph visualization, equipping analysts with a powerful tool to explore large graphs. We introduce Pathfinder, a technique that provides visual methods to query paths, while considering various constraints. The resulting set of paths is visualized in both a ranked list and as a node‐link diagram. For the paths in the list, we display rich attribute data associated with nodes and edges, and the node‐link diagram provides topological context. The paths can be ranked based on topological properties, such as path length or average node degree, and scores derived from attribute data. Pathfinder is designed to scale to graphs with tens of thousands of nodes and edges by employing strategies such as incremental query results. We demonstrate Pathfinder's fitness for use in scenarios with data from a coauthor network and biological pathways.     

Comparative Exploration of Document Collections: a Visual Analytics Approach

We present an analysis and visualization method for computing what distinguishes a given document collection from others. We determine topics that discriminate a subset of collections from the remaining ones by applying probabilistic topic modeling and subsequently approximating the two relevant criteria distinctiveness and characteristicness algorithmically through a set of heuristics. Furthermore, we suggest a novel visualization method called DiTop‐View, in which topics are represented by glyphs (topic coins) that are arranged on a 2D plane. Topic coins are designed to encode all information necessary for performing comparative analyses such as the class membership of a topic, its most probable terms and the discriminative relations. We evaluate our topic analysis using statistical measures and a small user experiment and present an expert case study with researchers from political sciences analyzing two real‐world datasets.     

Comparing Bar Chart Authoring with Microsoft Excel and Tangible Tiles

Providing tools that make visualization authoring accessible to visualization non‐experts is a major research challenge. Currently the most common approach to generating a visualization is to use software that quickly and automatically produces visualizations based on templates. However, it has recently been suggested that constructing a visualization with tangible tiles may be a more accessible method, especially for people without visualization expertise. There is still much to be learned about the differences between these two visualization authoring practices. To better understand how people author visualizations in these two conditions, we ran a qualitative study comparing the use of software to the use of tangible tiles, for the creation of bar charts. Close observation of authoring activities showed how each of the following varied according to the tool used: 1) sequences of action; 2) distribution of time spent on different aspects of the InfoVis pipeline; 3) pipeline task separation; and 4) freedom to manipulate visual variables. From these observations, we discuss the implications of the variations in activity sequences, noting tool design considerations and pointing to future research questions.     

Visualizing the Evolution of Community Structures in Dynamic Social Networks

Social network analysis is the study of patterns of interaction between social entities. The field is attracting increasing attention from diverse disciplines including sociology, epidemiology, and behavioral ecology. An important sociological phenomenon that draws the attention of analysts is the emergence of communities, which tend to form, evolve, and dissolve gradually over a period of time. Understanding this evolution is crucial to sociologists and domain scientists, and often leads to a better appreciation of the social system under study. Therefore, it is imperative that social network visualization tools support this task. While graph‐based representations are well suited for investigating structural properties of networks at a single point in time, they appear to be significantly less useful when used to analyze gradual structural changes over a period of time. In this paper, we present an interactive visualization methodology for dynamic social networks. Our technique focuses on revealing the community structure implied by the evolving interaction patterns between individuals. We apply our visualization to analyze the community structure in the US House of Representatives. We also report on a user study conducted with the participation of behavioral ecologists working with social network datasets that depict interactions between wild animals. Findings from the user study confirm that the visualization was helpful in providing answers to sociological questions as well as eliciting new observations on the social organization of the population under study.     

Supporting Exploratory Analysis with the Select & Slice Table

In interactive visualization, selection techniques such as dynamic queries and brushing are used to specify and extract items of interest. In other words, users define areas of interest in data space that often have a clear semantic meaning. We call such areas Semantic Zones, and argue that support for their manipulation and reasoning with them is highly useful during exploratory analysis. An important use case is the use of these zones across different subsets of the data, for instance to study the population of semantic zones over time. To support this, we present the Select & Slice Table. Semantic zones are arranged along one axis of the table, and data subsets are arranged along the other axis of the table. Each cell contains a set of items of interest from a data subset that matches the selection specifications of a zone. Items in cells can be visualized in various ways, as a count, as an aggregation of a measure, or as a separate visualization, such that the table gives an overview of the relationship between zones and data subsets. Furthermore, users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data subsets. We present two case studies to illustrate the support offered by the Select & Slice table during exploratory analysis of multivariate data.     

Exploring Items and Features with IF,FI‐Tables

The exploration of high‐dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an I^F‐Table, showing all items with a selection of features; and an F^I‐Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.     

Visual Inspection of Multivariate Graphs

Most graph visualization techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, we present a technique where this data plays a more central role. Nodes and edges are clustered based on associated data. Via direct manipulation users can interactively inspect and query the graph. Questions that can be answered include, “which edge types are activated by specific node attributes?” and, “how and from where can I reach specific types of nodes?” To validate our approach we contrast it with current practice. We also provide several examples where our method was used to study transition graphs that model real‐world systems.