Several techniques for visualization of dynamic graphs are based on different spatial arrangements of a temporal sequence of node-link diagrams. Many studies in the literature have investigated the importance of maintaining the user's mental map across this temporal sequence, but usually each layout is considered as a static graph drawing and the effect of user interaction is disregarded. We conducted a task-based controlled experiment to assess the effectiveness of two basic interaction techniques: the adjustment of the layout stability and the highlighting of adjacent nodes and edges. We found that generally both interaction techniques increase accuracy, sometimes at the cost of longer completion times, and that the highlighting outclasses the stability adjustment for many tasks except the most complex ones.
Besides the importance of preserving the mental map in node-link diagrams, another issue which attracts the interest of scholars is the comparison between animation approaches and timeline approaches, the latter being usually based on small multiples in a juxtaposition arrangement. The controlled experiment by Farrugia and Quigley @cite_12 found that static drawings outperform animation in terms of task completion time. @cite_42 , in an analogous user study, found that small multiples are generally faster, but more error-prone for certain tasks; moreover, mental map preservation has little influence on both response time and error rate. @cite_30 also conducted a comparative evaluation of animation versus small multiples in the context of flow maps. They found that with animation there were more findings of changes in adjacent time slices, where with small multiples there were more findings about longer time periods. Moreover, they suggest that switching from one view to the other might lead to an increase in the numbers of findings; we see this consideration in analogy with the mental map case, where the introduction of a stability slider might allow the user to adapt the layout to a particular task and possibly increase the overall visualization effectiveness.
@cite_30 @cite_42 @cite_12
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In this paper, we present the results of a human-computer interaction experiment that compared the performance of the animation of dynamic graphs to the presentation of small multiples and the effect that mental map preservation had on the two conditions. Questions used in the experiment were selected to test both local and global properties of graph evolution over time. The data sets used in this experiment were derived from standard benchmark data sets of the information visualization community. We found that small multiples gave significantly faster performance than animation overall and for each of our five graph comprehension tasks. In addition, small multiples had significantly more errors than animation for the tasks of determining sets of nodes or edges added to the graph during the same timeslice, although a positive time-error correlation coefficient suggests that, in this case, faster responses did not lead to more errors. This result suggests that, for these two tasks, animation is preferable if accuracy is more important than speed. Preserving the mental map under either the animation or the small multiples condition had little influence in terms of error rate and response time. We present a qualitative user study analyzing findings made while exploring changes over time in spatial interactions. We analyzed findings made by the study participants with flow maps, one of the most popular representations of spatial interactions, using animation and small-multiples as two alternative ways of representing temporal changes. Our goal was not to measure the subjects’ performance with the two views, but to find out whether there are qualitative differences between the types of findings users make with these two representations. To achieve this goal we performed a deep analysis of the collected findings, the interaction logs, and the subjective feedback from the users. We observed that with animation the subjects tended to make more findings concerning geographically local events and changes between subsequent years. With small-multiples more findings concerning longer time periods were made. Besides, our results suggest that switching from one view to the other might lead to an increase in the numbers of findings of specific types made by the subjects which can be beneficial for certain tasks. © 2012 Wiley Periodicals, Inc. Cascades appear in many applications, including biological graphs and social media analysis. In a cascade, a dynamic attribute propagates through a graph, following its edges. We present the results of a formal user study that tests the effectiveness of different types of cascade visualisations on node-link diagrams for the task of judging cascade spread. Overall, we found that a small multiples presentation was significantly faster than animation with no significant difference in terms of error rate. Participants generally preferred animation over small multiples and a hierarchical layout to a force-directed layout. Considering each presentation method separately, when comparing force-directed layouts to hierarchical layouts, hierarchical layouts were found to be significantly faster for both presentation methods and significantly more accurate for animation. Representing the history of the cascade had no significant effect. Thus, for our task, this experiment supports the use of a small multiples interf... Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between en- tities in readable, scalable, and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publica- tions. While static graph visualizations are often divided into node-link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline-based ones. Finally, we identify and discuss challenges for future research. Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node-link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline-based ones. A bibliographic analysis provides insights into the organization and development of the field and its community. Finally, we identify and discuss challenges for future research. We also provide feedback from experts, collected with a questionnaire, which gives a broad perspective of these challenges and the current state of the field. First we introduce the concept of graph animations as a sequence of evolving graphs and a generic algorithm which computes a Foresighted Layout for dynamically drawing these graphs while preserving the mental map. The algorithm is generic in the sense that it takes a static graph drawing algorithm as a parameter. In other words, trees can be animated with a static tree layouter, graphs with a static Sugiyama-style layouter or a spring embedder, etc. Second we discuss applications of Foresighted Layout in algorithm animation and visualization of navigation behaviour. 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. In this paper, we present a new approach to exploring dynamic graphs. We have developed a new clustering algorithm for dynamic graphs which finds an ideal clustering for each time-step and links the clusters together. The resulting time-varying clusters are then used to define two visual representations. The first view is an overview that shows how clusters evolve over time and provides an interface to find and select interesting time-steps. The second view consists of a node link diagram of a selected time-step which uses the clustering to efficiently define the layout. By using the time-dependant clustering, we ensure the stability of our visualization and preserve user mental map by minimizing node motion, while simultaneously producing an ideal layout for each time step. Also, as the clustering is computed ahead of time, the second view updates in linear time which allows for interactivity even for graphs with upwards of tens of thousands of nodes. 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. © 2012 Wiley Periodicals, Inc. Abstract Many models in software and information engineering use graph representations; examples are data flow diagrams, state transition diagrams, flow charts, PERT charts, organization charts, Petri nets and entity-relationship diagrams. The usefulness of these graph representations depends on the quality of the layout of the graphs. Automatic graph layout, which can release humans from graph drawing, is now available in several visualization systems. Most automatic layout facilities take a purely combinatorial description of a graph and produce a layout of the graph; these methods are called 'layout creation' methods. For interactive systems, another kind of layout is needed: a facility which can adjust a layout after a change is made by the user or by the application. Although layout adjustment is essential in interactive systems, most existing layout algorithms are designed for layout creation. The use of a layout creation method for layout adjustment may totally rearrange the layout and thus destroy the user's 'mental map' of the diagram; thus a set of layout adjustment methods, separate from layout creation methods, is needed. This paper discusses some layout adjustment methods and the preservation of the 'mental map' of the diagram. First, several models are proposed to make the concept of 'mental map' more precise. Then two kinds of layout adjustments are described. One is an algorithm for rearranging a diagram to avoid overlapping nodes, and the other is a method aimed at changing the focus of interest of the user without destroying the mental map. Next, some experience with visualization systems in which the techniques have been employed is also described. Most models are expressed and visualized with UML nowadays and most UML diagrams are based on graphs. Such graphs can get quite large if a project contains more than 600 class files, which is not uncommon. But changes are hard to recognize in a graph with 600 nodes. Therefore, the user has to find a way to build his own mental map based on the original graph. This mental map should give an overview of the original graph size, structure and it should point to changed areas.We present in this paper an approach, which gives the user a smaller graph as a mental map. This smaller graph uses packages as foundations for nodes and displays the package's content as the node icon. This had be done beforehand to visualize the evolution of developer dependencies over time, where the node icon presented the package's developer information as a pie chart. A suitable icon to present a package's class diagram can be a smaller version of the class diagram itself, if the layout respects its predecessors to allow the user an overview of its evolution. We present a usability study of two recent password manager proposals: PwdHash (, 2005) and Password Multiplier (, 2005). Both papers considered usability issues in greater than typical detail, the former briefly reporting on a small usability study; both also provided implementations for download. Our study involving 26 users found that both proposals suffer from major usability problems. Some of these are not "simply" usability issues, but rather lead directly to security exposures. Not surprisingly, we found the most significant problems arose from users having inaccurate or incomplete mental models of the software. Our study revealed many interesting misunderstandings D for example, users reporting a task as easy even when unsuccessful at completing that task; and believing their passwords were being strengthened when in fact they had failed to engage the appropriate protection mechanism. Our findings also suggested that ordinary users would be reluctant to optin to using these managers: users were uncomfortable with "relinquishing control" of their passwords to a manager, did not feel that they needed the password managers, or that the managers provided greater security. Maps offer a familiar way to present geographic data (continents, countries), and additional information (topography, geology), can be displayed with the help of contours and heat-map overlays. In this paper, we consider visualizing large-scale dynamic relational data by taking advantage of the geographic map metaphor. We describe a map-based visualization system which uses animation to convey dynamics in large data sets, and which aims to preserve the viewer's mental map while also offering readable views at all times. Our system is fully functional and has been used to visualize user traffic on the Internet radio station last.fm, as well as TV-viewing patterns from an IPTV service. All map images in this paper are available in high-resolution at [CHECK END OF SENTENCE] as are several movies illustrating the dynamic visualization. We investigate the readability of node-link diagrams for directed graphs when using partially drawn links instead of showing each link explicitly in its full length. Providing the complete link information between related nodes in a graph can lead to visual clutter caused by many edge crossings. To reduce visual clutter, we draw only partial links. Then, the question arises if such diagrams are still readable, understandable, and interpretable. As a step toward answering this question, we conducted a controlled user experiment with 42 participants to uncover differences in accuracy and completion time for three different tasks: identifying the existence of a direct link, the existence of an indirect connection with one intermediate node, and the node with the largest number of outgoing edges. Furthermore, we compared tapered and traditional edge representations, three different graph sizes, and six different link lengths. In all configurations, the nodes of the graph were placed according to the force-directed layout by Fruchterman and Reingold. One result of this study is that the characteristics of completion times and error rates depend on the type of task. A general observation is that partially drawn links can lead to shorter task completion times, which occurs for nearly all graph sizes, tasks, and both tapered and traditional edge representations. In contrast, there is a tendency toward higher error rates for shorter links, which in fact is task-dependent. Graph drawing algorithms have classically addressed the layout of static graphs. However, the need to draw evolving or dynamic graphs has brought into question many of the assumptions, conventions and layout methods designed to date. For example, social scientists studying evolving social networks have created a demand for visual representations of graphs changing over time. Two common approaches to represent temporal information in graphs include animation of the network and use of static snapshots of the network at different points in time. Here, we report on two experiments, one in a laboratory environment and another using an asynchronous remote web-based platform, Mechanical Turk, to compare the efficiency of animated displays versus static displays. Four tasks are studied with each visual representation, where two characterise overview level information presentation, and two characterise micro level analytical tasks. For the tasks studied in these experiments and within the limits of the experimental system, the results of this study indicate that static representations are generally more effective particularly in terms of time performance, when compared to fully animated movie representations of dynamic networks. Causality visualization is an important tool for many scientific domains that involve complex interactions between multiple entities (examples include parallel and distributed systems in computer science). However, traditional visualization techniques such as Hasse diagrams are not well-suited to large system executions, and users often have difficulties answering even basic questions using them, or have to spend inordinate amounts of time to do so. In this paper, we present the Growing Squares and Growing Polygons methods, two sibling visualization techniques that were designed to solve this problem by providing efficient 2D causality visualization through the use of color, texture, and animation. Both techniques have abandoned the traditional linear timeline and instead map the time parameter to the size of geometrical primitives representing the processes; in the Growing Squares case, each process is a color-coded square that receives color influences from other process squares as messages reach it; in the Growing Polygons case, each process is instead an n-sided polygon consisting of triangular sectors showing color-coded influences from the other processes. We have performed user studies of both techniques, comparing them with Hasse diagrams, and they have been shown to be significantly more efficient than old techniques, both in terms of objective performance as well as the subjective opinion of the test subjects (the Growing Squares technique is, however, only significantly more efficient for small systems). Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and communicate their findings. We present Voder , a system that lets users interact with automatically-generated data facts to explore both alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder's design and features, we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. We study the problem of the amount of information required to draw a complete or a partial map of a graph with unlabeled nodes and arbitrarily labeled ports. A mobile agent, starting at any node of an unknown connected graph and walking in it, has to accomplish one of the following tasks: draw a complete map of the graph, i.e., find an isomorphic copy of it including port numbering, or draw a partial map, i.e., a spanning tree, again with port numbering. The agent executes a deterministic algorithm and cannot mark visited nodes in any way. None of these map drawing tasks is feasible without any additional information, unless the graph is a tree. Hence we investigate the minimum number of bits of information (minimum size of advice) that has to be given to the agent to complete these tasks. It turns out that this minimum size of advice depends on the number n of nodes or the number m of edges of the graph, and on a crucial parameter @m, called the multiplicity of the graph, which measures the number of nodes that have an identical view of the graph. We give bounds on the minimum size of advice for both above tasks. For @m=1 our bounds are asymptotically tight for both tasks and show that the minimum size of advice is very small. For @m>1 the minimum size of advice increases abruptly. In this case our bounds are asymptotically tight for topology recognition and asymptotically almost tight for spanning tree construction. In dynamic graph drawing, the input is a sequence of graphs for which a sequence of layouts is to be generated such that the quality of individual layouts is balanced with layout stability over time. Qualitatively different extensions of drawing algorithms for static graphs to the dynamic case have been proposed, but little is known about their relative utility. We report on a quantitative study comparing the three prototypical extensions via their adaptation for the stress-minimization framework. While some findings are more subtle, the linking approach connecting consecutive instances of the same vertex is found to be the overall method of choice.