Graph Bundling by Kernel Density Estimation

Hurter, Christophe; Ersoy, Ozan; Telea, Alexandru

doi:10.1111/j.1467-8659.2012.03079.x

Cited by 138 publications

(267 citation statements)

References 26 publications

(68 reference statements)

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“…For instance, an approach called hierarchical edge bundling [3] uses hierarchy tree branches for edge routing; a method called geometric edge bundling [4] forms bundles by routing edges along a mesh generated by a triangulation algorithm; the force-directed edge bundling technique [5,6] splits the edges into segments that attract each other as a basis for bundling edges; the skeleton-based edge bundling method [7] generates a skeleton from the medial axes of groups of similar edges, and then attracts edges to this skeleton; and the kernel density estimation edge bundling method [8] computes a density map and moves the edges in order to create bundles.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Evolutionary Algorithm for an Optimization Model of Edge Bundling

2018

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This paper discusses three edge bundling optimization problems that aim to minimize the total number of bundles of a graph drawing, in conjunction with other aspects, as the main goal. A novel evolutionary algorithm for edge bundling for these problems is described. The algorithm was successfully tested by solving the related problems applied to real-world instances in reasonable computational time. The development and analysis of optimization models have received little attention in the area of edge bundling. However, the reported experimental results demonstrate the effectiveness and the applicability of the proposed evolutionary algorithm to help resolve edge bundling problems by formally defining them as optimization models.

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Section: Related Workmentioning

confidence: 99%

“…Instead, the bundles are considered as oriented-curved routes, each containing a set of edges. The related methods employ strategies for defining routes and making associations between them [5,8]. McKnight [10] affirms that many existing edge bundling approaches do not generate bundles directly (explicitly).…”

Section: Edge Bundling As An Optimization Problemmentioning

confidence: 99%

An Evolutionary Algorithm for an Optimization Model of Edge Bundling

2018

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“…Rather than relying on precomputation, new methods for graph visualization should investigate faster solutions like iteration or approximation to yield faster summary information (e.g., edge bundling by density estimation [86]). …”

Section: Research Directionsmentioning

confidence: 99%

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Pienta

Abello

Kahng

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Abstract-Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields -data mining, machine learning, human-computer ineraction, information visualization, information retrieval, and recommender systems -to underline their parallel and complementary contributions to graph sensemaking.We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.

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“…However, most advanced graph visualization techniques are developed based on network flows (e.g. [22], [23]), and thus are not suitable for communication graphs.…”

Section: Characterizing Communication Graphmentioning

confidence: 99%

CommGram: A New Visual Analytics Tool for Large Communication Trace Data

Zeng

et al. 2014

2014 First Workshop on Visual Performance Analysis

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Abstract-The performance of massively parallel program is often impacted by the cost of communication across computing nodes. Analysis of communication patterns is critical for understanding and optimizing massively parallel programs. Visualization can help identify potential communication bottlenecks by displaying message trace data. However, the visual clutter and temporal incoherence problems are typically incurred in existing visualization tools for a considerable number of processors. In this paper, we present a new tool, named CommGram, which supports visual analysis of communication patterns for massive parallel MPI programs. With the benefit of MPI trace library DUMPI of SST, our framework builds hierarchical clustering trees for computational community domain, and takes advantage of graphical user interface (GUI) to convey communication patterns at different levels of detail. The effectiveness of our tool is demonstrated using large-scale parallel applications.

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Graph Bundling by Kernel Density Estimation

Cited by 138 publications

References 26 publications

An Evolutionary Algorithm for an Optimization Model of Edge Bundling

An Evolutionary Algorithm for an Optimization Model of Edge Bundling

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

CommGram: A New Visual Analytics Tool for Large Communication Trace Data

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