Topology-aware navigation in large networks

Moscovich, Tomer; Chevalier, Fanny; Henry, Nathalie; Pietriga, Emmanuel; Fekete, Jean-Daniel

doi:10.1145/1518701.1519056

Cited by 112 publications

(105 citation statements)

References 34 publications

(33 reference statements)

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“…Additional relevant vertices and connections are revealed only on demand, based on graph structure or specialized degree-of-interest functions that can incorporate semantic importance or users' interaction histories [72,55,33,45,102,26]. Recently, such approaches have been extended to dynamic graphs by incorporating temporal histories, and applying relevancy filtering to a storyline-based representation [75].…”

Section: Dynamic Graph Analyticsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, its the structure of the dynamic graph that evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.

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Section: Dynamic Graph Analyticsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

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“…These actions can be performed directly on the network structure, such as link sliding [46] or bring-and-go [63]. These techniques allow the user to quickly find out-of-viewport nodes that are attached to a particular node, relocate these to be temporarily positioned in their current view and then allow further navigation from them.…”

Section: Navigationmentioning

confidence: 99%

Interaction in the Visualization of Multivariate Networks

Wybrow¹,

Elmqvist²,

Fekete³

et al. 2014

Multivariate Network Visualization

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Abstract. Interaction is a vital component in the visualization of multivariate networks. It enables greater amounts of information to be seen and explored than is possible with static visualization. Interaction can also help show the information landscape of the data while still allowing users to find and view areas of interest in greater detail and pivot between these. In this chapter we first discuss the design space and requirements for interacting with large multivariate data sets. We describe and classify relevant interaction techniques, and give examples of the interactive aspects of multivariate graph visualization systems. We present recommendations and guidelines for designing novel interaction approaches. Finally, we describe the open challenges within the field of multivariate graph visualization as we see them.

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“…It is well known that interacting with the graph through zooming, highlighting, or linked views provides significant insights [21]. Coordinated views have been proposed for visualizing graphs in the context of email communication networks [24].…”

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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Topology-aware navigation in large networks

Cited by 112 publications

References 34 publications

Temporal Multivariate Networks

Temporal Multivariate Networks

Interaction in the Visualization of Multivariate Networks

Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations

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