Edge Bundling Research Articles

Enhancing graph drawings through edge bundling using clustering ensembles

Edge bundling is a technique used to improve the readability of large graph drawings by grouping edges to reduce visual complexity. This paper treats this task as a clustering problem, using compatibility metrics to evaluate solutions in an optimization pipeline combined with a clustering ensemble approach. The aim is to present the Clustering Ensemble-based Edge Bundling (CEBEB) method for solving the General-based Edge Bundling (GBEB) problem and report results for some given graphs. The CEBEB method proved very promising and generated better solutions than an existing evolutionary algorithm. Additionally, the paper introduces a new ensemble algorithm, specific for the GBEB, and reviews some previous results.

Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts

Graph visualization has been successfully applied in a wide range of problems and applications. Although different approaches are available to create visual representations, most of them suffer from clutter when faced with many nodes and/or edges. Among the techniques that address this problem, edge bundling has attained relative success in improving node-link layouts by bending and aggregating edges. Despite their success, most approaches perform the bundling based only on visual space information. There is no explicit connection between the produced bundled visual representation and the underlying data (edges or vertices attributes). In this paper, we present a novel edge bundling technique, called Similarity-Driven Edge Bundling (SDEB), to address this issue. Our method creates a similarity hierarchy based on a multilevel partition of the data, grouping edges considering the similarity between nodes to guide the bundling. The novel features introduced by SDEB are explored in different application scenarios, from dynamic graph visualization to multilevel exploration. Our results attest that SDEB produces layouts that consistently follow the similarity relationships found in the graph data, resulting in semantically richer presentations that are less cluttered than the state-of-the-art.

Voxel-based edge bundling through direction-aware kernel smoothing

Relational data with a spatial embedding and depicted as node-link diagram is very common, e.g, in neuroscience, and edge bundling is one way to increase its readability or reveal hidden structures. This article presents a 3D extension to kernel density estimation-based edge bundling that is meant to be used in an interactive immersive analysis setting. This extension adds awareness of the edges’ direction when using kernel smoothing and thus implicitly supports both directed and undirected graphs. The method generates explicit bundles of edges, which can be analyzed and visualized individually and as sufficient as possible for a given application context, while it scales linearly with the input size.

Route‐Aware Edge Bundling for Visualizing Origin‐Destination Trails in Urban Traffic

AbstractOrigin‐destination (OD) trails describe movements across space. Typical visualizations thereof use either straight lines or plot the actual trajectories. To reduce clutter inherent to visualizing large OD datasets, bundling methods can be used. Yet, bundling OD trails in urban traffic data remains challenging. Two specific reasons hereof are the constraints implied by the underlying road network and the difficulty of finding good bundling settings. To cope with these issues, we propose a new approach called Route Aware Edge Bundling (RAEB). To handle road constraints, we first generate a hierarchical model of the road‐and‐trajectory data. Next, we derive optimal bundling parameters, including kernel size and number of iterations, for a user‐selected level of detail of this model, thereby allowing users to explicitly trade off simplification vs accuracy. We demonstrate the added value of RAEB compared to state‐of‐the‐art trail bundling methods on both synthetic and real‐world traffic data for tasks that include the preservation of road network topology and the support of multiscale exploration.

The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs.

Exploring coordinated relationships (e.g., shared relationships between two sets of entities) is an important analytics task in a variety of real-world applications, such as discovering similarly behaved genes in bioinformatics, detecting malware collusions in cyber security, and identifying products bundles in marketing analysis. Coordinated relationships can be formalized as biclusters. In order to support visual exploration of biclusters, bipartite graphs based visualizations have been proposed, and edge bundling is used to show biclusters. However, it suffers from edge crossings due to possible overlaps of biclusters, and lacks in-depth understanding of its impact on user exploring biclusters in bipartite graphs. To address these, we propose a novel bicluster-based seriation technique that can reduce edge crossings in bipartite graphs drawing and conducted a user experiment to study the effect of edge bundling and this proposed technique on visualizing biclusters in bipartite graphs. We found that they both had impact on reducing entity visits for users exploring biclusters, and edge bundles helped them find more justified answers. Moreover, we identified four key trade-offs that inform the design of future bicluster visualizations. The study results suggest that edge bundling is critical for exploring biclusters in bipartite graphs, which helps to reduce low-level perceptual problems and support high-level inferences.

Scanpath visualization and comparison using visual aggregation techniques.

We demonstrate the use of different visual aggregation techniques to obtain non-cluttered visual representations of scanpaths. First, fixation points are clustered using the mean-shift algorithm. Second, saccades are aggregated using the Attribute-Driven Edge Bundling (ADEB) algorithm that handles a saccades direction, onset timestamp, magnitude or their combination for the edge compatibility criterion. Flow direction maps, computed during bundling, can be visualized separately (vertical or horizontal components) or as a single image using the Oriented Line Integral Convolution (OLIC) algorithm. Furthermore, cosine similarity between two flow direction maps provides a similarity map to compare two scanpaths. Last, we provide examples of basic patterns, visual search task, and art perception. Used together, these techniques provide valuable insights about scanpath exploration and informative illustrations of the eye movement data.

ADDRESSING A FUTURE WITH DATA VISUALIZATION ON SCIENCE FICTION MOVIES: DYSTOPIA OR UTOPIA

While the far-fetched ideas brought up with imagination are shedding lights on Science Fiction Movies, occasionally ever-growing technology has been fuelled by these creative inspirations and imaginations. This article reflects on the findings of the data visualization project Dystopia-Utopia ( www.dystopia-utopia.com) and investigates the existence of a common discourse that would stand out in Science Fiction Movies as a significant social modifier. In this approach with the use of a built interactive Circular Hierarchial Edge Bundling Data Visualization system, the data provided by the online participants have been analyzed and the results show that Dystopia theme has been more frequently spoken in the discourse of mainstream Science Fiction Movies.

Multi-type Edge Bundling in Force-Directed Layout and Evaluation

Abstract Numerous information visualization techniques are available for utilizing and analyzing large data. Among these techniques, network visualization, which employs node-link diagrams, can determine the relationship among multi-dimensional data. However, when data become extremely large, visualization becomes obscure because of visual clutter. To address this problem, many edge bundling techniques have been proposed. However, although graphs present several edge types, previous techniques do not reflect these edges. In this paper, we propose a new edge bundling method for multi-type co-occurring graphs. In this method, electro-static forces work between each pair of edges; however, if the edges are of different types, then repulsion works between pairs. By bundling edges of the same type, a graph can more clearly show relationships among data. Qualitative evaluation through questionnaires lead to useful knowledge, i.e., the proposed method improves bundling performance more extensively than other related work.