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

Nguyen, Quan; Hong, Seok‐Hee; Eades, Peter

doi:10.1007/978-3-642-25878-7_13

Cited by 21 publications

(34 citation statements)

References 19 publications

(24 reference statements)

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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%

“…The aggregate compatibility value is typically addressed in a so-called multiplicative model: C = 1..k C i in a number of works [20,21,24,28]. However, for stream bundling we propose several alternatives for aggregating compatibilities:…”

Section: Aggregating Compatibilitymentioning

confidence: 99%

“…Spatial compatibility has still been intensively studied but mainly for edge bundling of static graphs [21,24,28]. Here we extend the notion to denote the spatial compatibility S(e i , e j ) of a pair of stream elements.…”

Section: Compatibility Metricsmentioning

confidence: 99%

“…Several works adapted FDEB for different bundling criteria in various application domains [21,24,28].…”

mentioning

confidence: 99%

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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%

Section: Aggregating Compatibilitymentioning

confidence: 99%

Section: Compatibility Metricsmentioning

confidence: 99%

“…Several works adapted FDEB for different bundling criteria in various application domains [21,24,28].…”

mentioning

confidence: 99%

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StreamEB: Stream Edge Bundling.

2013

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“…There are several measures including: geometric, semantic, importance, topology, neighborhood, temporal and connectivity [5,6,[21][22][23].…”

Section: Compatibility Measuresmentioning

confidence: 99%

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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Stub Bundling and Confluent Spirals for Geographic Networks

Nocaj

Brandes

2013

Graph Drawing

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Edge bundling is a technique to reduce clutter by routing parts of several edges along a shared path. In particular, it is used for visualization of geographic networks where vertices have fixed coordinates. Two main drawbacks of the common approach of bundling the interior of edges are that (i) tangents at endpoints deviate from the line connecting the two endpoints in an uncontrolled way and (ii) there is ambiguity as to which pairs of vertices are actually connected. Both severely reduce the interpretability of geographic network visualizations. We therefore propose methods that bundle edges at their ends rather than their interior. This way, tangents at vertices point in the general direction of all neighbors of edges in the bundle, and ambiguity is avoided altogether. For undirected graphs our approach yields curves with no more than one turning point. For directed graphs we introduce a new drawing style, confluent spiral drawings, in which the direction of edges can be inferred from monotonically increasing curvature along each spiral segment. Research supported in part by DFG under grant GRK 1024. We are grateful to Sabine Cornelsen for valuable comments and suggestions on earlier drafts of this paper.

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

Cited by 21 publications

References 19 publications

StreamEB: Stream Edge Bundling.

StreamEB: Stream Edge Bundling.

An Evolutionary Algorithm for an Optimization Model of Edge Bundling

Stub Bundling and Confluent Spirals for Geographic Networks

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