Motif-Based Visual Analysis of Dynamic Networks
2022-08-25T08:27:36Z, Cakmak, Eren, Fuchs, Johannes, Jäckle, Dominik, Schreck, Tobias, Brandes, Ulrik, Keim, Daniel A.
Many data analysis problems rely on dynamic networks, such as social or communication network analyses. Providing a scalable overview of long sequences of such dynamic networks remains challenging due to the underlying large-scale data containing elusive topological changes. We propose two complementary pixel-based visualizations, which reflect occurrences of selected sub-networks (motifs) and provide a time-scalable overview of dynamic networks: a network-level census (motif significance profiles) linked with a node-level sub-network metric (graphlet degree vectors) views to reveal structural changes, trends, states, and outliers. The network census captures significantly occurring motifs compared to their expected occurrences in random networks and exposes structural changes in a dynamic network. The sub-network metrics display the local topological neighborhood of a node in a single network belonging to the dynamic network. The linked pixel-based visualizations allow exploring motifs in different-sized networks to analyze the changing structures within and across dynamic networks, for instance, to visually analyze the shape and rate of changes in the network topology. We describe the identification of visual patterns, also considering different reordering strategies to emphasize visual patterns. We demonstrate the approach's usefulness by a use case analysis based on real-world large-scale dynamic networks, such as the evolving social networks of Reddit or Facebook.
Revealing the Invisible : Visual Analytics and Explanatory Storytelling for Advanced Team Sport Analysis
2018-10, Stein, Manuel, Breitkreutz, Thorsten, Häußler, Johannes, Seebacher, Daniel, Niederberger, Christoph, Schreck, Tobias, Grossniklaus, Michael, Keim, Daniel A., Janetzko, Halldor
The analysis of invasive team sports often concentrates on cooperative and competitive aspects of collective movement behavior. A main goal is the identification and explanation of strategies, and eventually the development of new strategies. In visual sports analytics, a range of different visual-interactive analysis techniques have been proposed, e.g., based on visualization using for example trajectories, graphs, heatmaps, and animations. Identifying suitable visualizations for a specific situation is key to a successful analysis. Existing systems enable the interactive selection of different visualization facets to support the analysis process. However, an interactive selection of appropriate visualizations is a difficult, complex, and time-consuming task. In this paper, we propose a four-step analytics conceptual workflow for an automatic selection of appropriate views for key situations in soccer games. Our concept covers classification, specification, explanation, and alteration of match situations, effectively enabling the analysts to focus on important game situations and the determination of alternative moves. Combining abstract visualizations with real world video recordings by Immersive Visual Analytics and descriptive storylines, we support domain experts in understanding key situations. We demonstrate the usefulness of our proposed conceptual workflow via two proofs of concept and evaluate our system by comparing our results to manual video annotations by domain experts. Initial expert feedback shows that our proposed concept improves the understanding of competitive sports and leads to a more efficient data analysis.
Dynamic Visual Abstraction of Soccer Movement
2017-07-04, Sacha, Dominik, Al-Masoudi, Feeras, Stein, Manuel, Schreck, Tobias, Keim, Daniel A., Andrienko, Gennady, Janetzko, Halldor
Trajectory-based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on-the-fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi-automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer.
dg2pix : Pixel-Based Visual Analysis of Dynamic Graphs
2020, Cakmak, Eren, Jäckle, Dominik, Schreck, Tobias, Keim, Daniel A.
Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. We propose dg2pix, a novel pixel-based visualization technique, to visually explore temporal and structural properties in long sequences of large-scale graphs. The approach consists of three main steps: (1) the multiscale modeling of the temporal dimension; (2) unsupervised graph embeddings to learn low-dimensional representations of the dynamic graph data; and (3) an interactive pixel-based visualization to simultaneously explore the evolving data at different temporal aggregation scales. dg2pix provides a scalable overview of a dynamic graph, supports the exploration of long sequences of high-dimensional graph data, and enables the identification and comparison of similar temporal states. We show the applicability of the technique to synthetic and real-world datasets, demonstrating that temporal patterns in dynamic graphs can be identified and interpreted over time. dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end and matrix representations on the low detail end.
SocialOcean : Visual Analysis and Characterization of Social Media Bubbles
2018, Diehl, Alexandra, Hundt, Michael, Häußler, Johannes, Seebacher, Daniel, Chen, Siming, Cilasun, Nida, Keim, Daniel A., Schreck, Tobias
Social media allows citizens, corporations, and authorities to create, post, and exchange information. The study of its dynamics will enable analysts to understand user activities and social group characteristics such as connectedness, geospatial distribution, and temporal behavior. In this context, social media bubbles can be defined as social groups that exhibit certain biases in social media. These biases strongly depend on the dimensions selected in the analysis, for example, topic affinity, credibility, sentiment, and geographic distribution. In this paper, we present SocialOcean, a visual analytics system that allows for the investigation of social media bubbles. There exists a large body of research in social sciences which identifies important dimensions of social media bubbles (SMBs). While such dimensions have been studied separately, and also some of them in combination, it is still an open question which dimensions play the most important role in defining SMBs. Since the concept of SMBs is fairly recent, there are many unknowns regarding their characterization. We investigate the thematic and spatiotemporal characteristics of SMBs and present a visual analytics system to address questions such as: What are the most important dimensions that characterize SMBs? and How SMBs embody in the presence of specific events that resonate with them? We illustrate our approach using three different real scenarios related to the single event of Boston Marathon Bombing, and political news about Global Warming. We perform an expert evaluation, analyze the experts' feedback, and present the lessons learned.
Tackling Similarity Search for Soccer Match Analysis : Multimodal Distance Measure and Interactive Query Definition
2018, Stein, Manuel, Janetzko, Halldor, Schreck, Tobias, Keim, Daniel A.
Analysts and coaches in soccer sports need to investigate large sets of past matches of opposing teams in short time to prepare their teams for upcoming matches. Thus, they need appropriate methods and systems supporting them in searching for soccer moves for comparison and explanation. For the search of similar soccer moves, established distance and similarity measures typically only take spatio-temporal features like shape and speed of movement into account. However, movement in invasive team sports such as soccer, includes much more than just a sequence of spatial locations. We survey the current state-of-the-art in trajectory distance measures and subsequently propose an enhanced similarity measure integrating spatial, player, event as well as high level context such as pressure into the process of similarity search. We present a visual search system supporting analysts in interactively identifying similar contextual enhanced soccer moves in a dataset containing more than 60 soccer matches. Our approach is evaluated by several expert studies. The results of the evaluation reveal the large potential of enhanced similarity measures in the future.
FDive : Learning Relevance Models Using Pattern-based Similarity Measures
2019, Dennig, Frederik L., Polk, Tom, Lin, Zudi, Schreck, Tobias, Pfister, Hanspeter, Behrisch, Michael
The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDive enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.
Visual Analysis of Urban Traffic Data based on High-Resolution and High-Dimensional Environmental Sensor Data
2018, Häußler, Johannes, Stein, Manuel, Seebacher, Daniel, Janetzko, Halldor, Schreck, Tobias, Keim, Daniel A.
Urbanization is an increasing global trend resulting in a strong increase in public and individual transportation needs. Accordingly, a major challenge for traffic and urban planners is the design of sustainable mobility concepts to maintain and increase the long-term health of humans by reducing environmental pollution. Recent developments in sensor technology allow the precise tracking of vehicle sensor information, allowing a closer and more in-depth analysis of traffic data. We propose a visual analytics system for the exploration of environmental factors in these high-resolution and high-dimensional mobility sensor data. Additionally, we introduce an interactive visual logging approach to enable experts to cope with complex interactive analysis processes and the problem of the reproducibility of results. The usefulness of our approach is demonstrated via two expert studies with two domain experts from the field of environment-related projects and urban traffic planning.
Analysis and Comparison of Feature-Based Patterns in Urban Street Networks
2017-08-09, Shao, Lin, Mittelstädt, Sebastian, Goldblatt, Ran, Omer, Itzhak, Bak, Peter, Schreck, Tobias
Analysis of street networks is a challenging task, needed in urban planning applications such as urban design or transportation network analysis. Typically, different network features of interest are used for within- and between comparisons across street networks. We introduce StreetExplorer, a visual-interactive system for analysis and comparison of global and local patterns in urban street networks. The system uses appropriate similarity functions to search for patterns, taking into account topological and geometric features of a street network. We enhance the visual comparison of street network patterns by a suitable color-mapping and boosting scheme to visualize the similarity between street network portions and the distribution of network features. Together with experts from the urban morphology domain, we apply our approach to analyze and compare two urban street networks, identifying patterns of historic development and modern planning approaches, demonstrating the usefulness of StreetExplorer.