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34
TopoLayout: Multilevel graph layout by topological features
 IEEE TRANS. VISUALIZATION AND COMPUTER GRAPHICS
, 2007
"... We describe TopoLayout, a featurebased,
multilevel algorithm that draws undirected graphs based on the topological features they contain. Topological features are detected recursively inside the graph, and their subgraphs are collapsed into single nodes, forming a graph hierarchy. Each feature is ..."
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Cited by 35 (5 self)
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We describe TopoLayout, a featurebased,
multilevel algorithm that draws undirected graphs based on the topological features they contain. Topological features are detected recursively inside the graph, and their subgraphs are collapsed into single nodes, forming a graph hierarchy. Each feature is drawn with an algorithm tuned for its topology. As would be expected from a featurebased approach, the runtime and visual quality of TopoLayout depends on the number and types of topological features present in the graph. We show experimental results comparing speed and visual quality for TopoLayout against four other multilevel algorithms on a variety of datasets with a range of connectivities and sizes. TopoLayout frequently improves the results in terms of speed and visual quality on these datasets.
Online Dynamic Graph Drawing
"... This paper presents an algorithm for drawing a sequence of graphs online. The algorithm strives to maintain the global structure of the graph and thus the user’s mental map, while allowing arbitrary modifications between consecutive layouts. The algorithm works online and uses various execution cu ..."
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Cited by 34 (1 self)
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This paper presents an algorithm for drawing a sequence of graphs online. The algorithm strives to maintain the global structure of the graph and thus the user’s mental map, while allowing arbitrary modifications between consecutive layouts. The algorithm works online and uses various execution culling methods in order to reduce the layout time and handle large dynamic graphs. Techniques for representing graphs on the GPU allow a speedup by a factor of up to 17 compared to the CPU implementation. The scalability of the algorithm across GPU generations is demonstrated. Applications of the algorithm to the visualization of discussion threads in Internet sites and to the visualization of social networks are provided.
Multilevel graph layout on the GPU
 IEEE TRANS. VIS. COMPUT. GRAPH
, 2007
"... This paper presents a new algorithm for force directed graph layout on the GPU. The algorithm, whose goal is to compute layouts accurately and quickly, has two contributions. The first contribution is proposing a general multilevel scheme, which is based on spectral partitioning. The second contri ..."
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Cited by 21 (1 self)
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This paper presents a new algorithm for force directed graph layout on the GPU. The algorithm, whose goal is to compute layouts accurately and quickly, has two contributions. The first contribution is proposing a general multilevel scheme, which is based on spectral partitioning. The second contribution is computing the layout on the GPU. Since the GPU requires a data parallel programming model, the challenge is devising a mapping of a naturally unstructured graph into a wellpartitioned structured one. This is done by computing a balanced partitioning of a general graph. This algorithm provides a general multilevel scheme, which has the potential to be used not only for computation on the GPU, but also on emerging multicore architectures. The algorithm manages to compute high quality layouts of large graphs in a fraction of the time required by existing algorithms of similar quality. An application for visualization of the topologies of ISP (Internet Service Provider) networks is presented. Index Terms—Graph layout, GPU, graph partitioning.
GrouseFlocks: Steerable exploration of graph hierarchy space
 IEEE TRANS. ON VISUALIZATION AND COMPUTER GRAPHICS
, 2008
"... Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domainspecific attributes associated with ..."
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Cited by 21 (7 self)
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Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domainspecific attributes associated with the nodes and edges which could be used to create many possible hierarchies providing unique views of the input graph. GrouseFlocks is a system for the exploration of this graph hierarchy space. By allowing users to see several different possible hierarchies on the same graph, the system helps users investigate graph hierarchy space instead of a single, fixed hierarchy. GrouseFlocks provides a simple set of operations so that users can create and modify their graph hierarchies based on selections. These selections can be made manually or based on patterns in the attribute data provided with the graph. It provides feedback to the user within seconds, allowing interactive exploration of this space.
ZAME: Interactive Largescale Graph Visualization
"... We present the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and p ..."
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Cited by 17 (6 self)
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We present the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higherorder metarepresentations. Aggregates are arranged into a pyramid hierarchy that allows for ondemand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia—over 500,000 articles and 6,000,000 links—with interactive performance on standard consumerlevel computer hardware.
Visualization of social and other scalefree networks
 IN PROC. OF IEEE INFOVIS
, 2008
"... This paper proposes novel methods for visualizing specifically the large powerlaw graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more ..."
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Cited by 17 (1 self)
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This paper proposes novel methods for visualizing specifically the large powerlaw graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the network’s underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scalefree graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.
Energy Models for Graph Clustering
"... The cluster structure of many realworld graphs is of great interest, as the clusters may correspond e.g. to communities in social networks or to cohesive modules in software systems. Layouts can naturally represent the cluster structure of graphs by grouping densely connected nodes and separating s ..."
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Cited by 15 (1 self)
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The cluster structure of many realworld graphs is of great interest, as the clusters may correspond e.g. to communities in social networks or to cohesive modules in software systems. Layouts can naturally represent the cluster structure of graphs by grouping densely connected nodes and separating sparsely connected nodes. This article introduces two energy models whose minimum energy layouts represent the cluster structure, one based on repulsion between nodes (like most existing energy models) and one based on repulsion between edges. The latter model is not biased towards grouping nodes with high degrees, and is thus more appropriate for the many realworld graphs with rightskewed degree distributions. The two energy models are shown to be closely related to widely used quality criteria for graph clusterings – namely the density of the cut, Shi and Malik’s normalized cut, and Newman’s modularity – and to objective functions optimized by eigenvectorbased graph drawing methods.
Winding roads: Routing edges into bundles
, 2010
"... Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently, it is difficult toreadtherelationshipsbetweennodesandthe highlevel edgepatternsth ..."
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Cited by 7 (2 self)
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Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently, it is difficult toreadtherelationshipsbetweennodesandthe highlevel edgepatternsthatmayexistinstandard node link diagram representations. Edge bundling techniques have been proposed to help solve this issue, which rely on high quality edge rerouting. In this paper, we introduce an intuitive edge bundling technique which efficiently reduces edge clutter in graphs drawings. Our method is based on the use of a grid built using the original graph to compute the edge rerouting. In comparison with previously proposed edge bundling methods, our technique improves both the level of clutter reduction and the computation performance. The second contribution of this paper is a GPUbased rendering method which helps users perceive bundles densities while preserving edge color.
Process and pitfalls in writing information visualization research papers
 Lecture Notes in Computer Science 2008; 4950
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