Level-of-detail visualization of clustered graph layouts

Balzer, Michael; Deußen, Oliver

doi:10.1109/apvis.2007.329288

Cited by 64 publications

(55 citation statements)

References 15 publications

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“…Numerous other community or cluster based layout algorithms have been proposed, including the energy model proposed by Truong et al [13], Frishman and Tal [3], and Balzer and Deussen [1]. Our proposed methods differ from previous approaches in that we do not require any userdefined parameters to produce our community layouts, nor do we focus on any specific aesthetic quality.…”

Section: Related Workmentioning

confidence: 95%

Visualizing Community Centric Network Layouts

Fagnan

Zaı̈ane

Goebel

2012

2012 16th International Conference on Information Visualisation

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We present our COMmunity Boundary (COMB) and COMmunity Circles (COMC) network layout algorithms that focus on revealing the structure of discovered communities and the relationships between these communities. We believe this information is vital when developing new community mining algorithms as it allows the viewer to more quickly assess the quality of a mining result without appealing to large tables of statistics. To implement our algorithms we have introduced numerous modifications to the existing Fruchterman-Reingold layout, including support for multi-sized vertices, removal of the bounding frame, introduction of circular bounding boxes, and a novel slotting system. Our evaluation argues that both COMB and COMC outperform existing alternatives in their ability to reveal community structure and emphasize inter-community relations.

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Section: Related Workmentioning

confidence: 95%

Visualizing Community Centric Network Layouts

Fagnan

Zaı̈ane

Goebel

2012

2012 16th International Conference on Information Visualisation

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“…Balzer et al [4] proposed a multi-level compound visualisation using transparent surfaces and edge bundling for a hierarchical 3D visualisation.…”

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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“…The importance of edge and connectivity conservation in understanding paths when using a graph hierarchy. A more complete version is presented in GrouseFlocks [6] of complex software in three dimensions using level of detail techniques [10]. All of these techniques use a static layout that is computed once up front, and a static hierarchy computed using the position of the vertices in the drawing.…”

Section: Existing Layout Requiredmentioning

confidence: 99%

Tugging Graphs Faster: Efficiently Modifying Path-Preserving Hierarchies for Browsing Paths

Archambault

Munzner

2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-Many graph visualization systems use graph hierarchies to organize a large input graph into logical components. These approaches detect features globally in the data and place these features inside levels of a hierarchy. However, this feature detection is a global process and does not consider nodes of the graph near a feature of interest. TugGraph is a system for exploring paths and proximity around nodes and subgraphs in a graph. The approach modifies a pre-existing hierarchy in order to see how a node or subgraph of interest extends out into the larger graph. It is guaranteed to create path-preserving hierarchies, so that the abstraction shown is meaningful with respect to the underlying structure of the graph. The system works well on graphs of hundreds of thousands of nodes and millions of edges. TugGraph is able to present views of this proximal information in the context of the entire graph in seconds, and does not require a layout of the full graph as input.

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Level-of-detail visualization of clustered graph layouts

Cited by 64 publications

References 15 publications

Visualizing Community Centric Network Layouts

Visualizing Community Centric Network Layouts

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

Tugging Graphs Faster: Efficiently Modifying Path-Preserving Hierarchies for Browsing Paths

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