Multilevel agglomerative edge bundling for visualizing large graphs

Gansner, Emden R.; Hu, Yifan; North, Stephen C.; Scheidegger, Carlos

doi:10.1109/pacificvis.2011.5742389

Cited by 142 publications

(173 citation statements)

References 15 publications

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“…Edge bundling has been quite successful in reducing clutter and has been extensively studied including hierarchical edge bundling [19], geometry-based edge clustering [13,19,22,31], force-directed edge bundling [20,21,24,28] and multi-level agglomative edge bundling [17]. However, most existing bundling techniques are not concerned with dynamic graphs "directly".…”

Section: Edge Bundlingmentioning

confidence: 99%

StreamEB: Stream Edge Bundling.

2013

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Abstract. Graph streams have been studied extensively, such as for data mining, while fairly limitedly for visualizations. Recently, edge bundling promises to reduce visual clutter in large graph visualizations, though mainly focusing on static graphs.This paper presents a new framework, namely StreamEB, for edge bundling of graph streams, which integrates temporal, neighbourhood, data-driven and spatial compatibility for edges. Amongst these metrics, temporal and neighbourhood compatibility are introduced for the first time. We then present force-directed and tree-based methods for stream edge bundling.

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Section: Edge Bundlingmentioning

confidence: 99%

StreamEB: Stream Edge Bundling.

2013

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“…Recently, Gansner et al [10] introduced a multi-level method which approximates k-neighbor edge proximity graphs using kd-tree as input for their agglomerative bundling algorithm. They reported experiments on the approach up to one million edges in a few minutes.…”

Section: Related Workmentioning

confidence: 99%

TGI-EB: A New Framework for Edge Bundling Integrating Topology, Geometry and Importance

2012

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Abstract. Edge bundling methods became popular for visualising large dense networks; however, most of previous work mainly relies on geometry to define compatibility between the edges.In this paper, we present a new framework for edge bundling, which tightly integrates topology, geometry and importance. In particular, we introduce new edge compatibility measures, namely importance compatibility and topology compatibility. More specifically, we present four variations of force directed edge bundling method based on the framework: Centrality-based bundling, Radial bundling, Topology-based bundling, and Orthogonal bundling.Our experimental results with social networks, biological networks, geographic networks and clustered graphs indicate that our new framework can be very useful to highlight the most important topological skeletal structures of the input networks.

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“…General graph visualization has been a long standing research problem and we refer the reader to Herman et al [15] for an excellent survey. However, most of these techniques, including hierarchical clustering [26], projections into lower dimensional spaces [7], topologically-driven layouts [2], or edge bundling [11], are primarily concerned with the creating graph layouts to highlight the topology of a graph. In our case, the topology of the graph-a 3D torus-is well known and creating an unstructured layout may obscure this fact.…”

Section: Visualizations Of Network Trafficmentioning

confidence: 99%

Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations

Landge

Levine

Bhatelé

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Network traffic resulting from two different runs of the parallel simulation pF3D. This simulation models laser plasma interaction inside of a hohlraum chamber by decomposing the domain into a set of blocks (left). Depending on how data blocks are mapped to processor cores (middle), different communication patterns occur. When staggering data placement (bottom right) we observe significantly more balanced communication compared to a default mapping similar to how the domain is decomposed (top right).Abstract-The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D's performance on an IBM Blue Gene/P system.

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Multilevel agglomerative edge bundling for visualizing large graphs

Cited by 142 publications

References 15 publications

StreamEB: Stream Edge Bundling.

StreamEB: Stream Edge Bundling.

TGI-EB: A New Framework for Edge Bundling Integrating Topology, Geometry and Importance

Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations

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