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134
M.: Graph visualization and navigation in information visualization: A survey
 IEEE Transactions on Visualization and Computer Graphics
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Mapping and Visualizing the Internet
 In Proceedings of the 2000 USENIX Annual Technical Conference
, 2000
"... We have been collecting and recording routing paths from a test host to each of over 90,000 registered networks on the Internet since August 1998. The resulting database contains interesting routing and reachability information, and is available to the public for research purposes. The daily scan ..."
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Cited by 113 (2 self)
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We have been collecting and recording routing paths from a test host to each of over 90,000 registered networks on the Internet since August 1998. The resulting database contains interesting routing and reachability information, and is available to the public for research purposes. The daily scans cover approximately a tenth of the networks on the Internet, with a full scan run roughly once a month. We have also been collecting Lucent's intranet data, and applied these tools to understanding its size and connectivity. We have also detecting the loss of power to routers in Yugoslavia as the result of NATO bombing. A simulated springforce algorithm lays out the graphs that results from these databases. This algorithm is well known, but has never been applied to such a large problem. The Internet graph, with around 88,000 nodes and 100,000 edges, is much larger than those previously considered tractable by the data visualization community. The resulting Internet layouts are pleasant, though rather cluttered. On smaller networks, like Lucent's intranet, the layouts present the data in a useful way. For the Internet data, we have tried plotting a minimum distance spanning tree; by throwing away edges, the remaining graph can be made more accessible. Once a layout is chosen, it can be colored in various ways to show networkrelevant data, such as IP address, domain information, location, ISPs, location of firewalls, etc. This paper expands and updates the description of the project given in [2]. 1
Metrics for Graph Drawing Aesthetics
 AUSTRALIAN COMPUTER SOCIETY
, 2001
"... Graph layout algorithms typically conform to one or more aesthetic criteria (e.g. minimising the number of bends, maximising orthogonality). Determining the extent to which a graph drawing conforms to an aesthetic criterion tends to be done informally, and varies between different algorithms. This p ..."
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Cited by 101 (2 self)
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Graph layout algorithms typically conform to one or more aesthetic criteria (e.g. minimising the number of bends, maximising orthogonality). Determining the extent to which a graph drawing conforms to an aesthetic criterion tends to be done informally, and varies between different algorithms. This paper presents formal metrics for measuring the aesthetic presence in a graph drawing for seven common aesthetic criteria, applicable to any graph drawing of any size. The metrics are useful for determining the aesthetic quality of a given graph drawing, or for defining a cost function for genetic algorithms or simulated annealing programs. The metrics are continuous, so that aesthetic quality is not stated as a binary conformance decision (i.e. the drawing either conforms to the aesthetic or not), but can be stated as the extent of aesthetic conformance using a number between 0 and 1. The paper presents the seven metric formulae. The application of these metrics is demonstrated through the aesthetic analysis of example graph drawings produced by common layout algorithms.
A Fast MultiScale Method for Drawing Large Graphs
 JOURNAL OF GRAPH ALGORITHMS AND APPLICATIONS
, 2002
"... We present a multiscale layout algorithm for the aesthetic drawing of undirected graphs with straightline edges. The algorithm is extremely fast, and is capable of drawing graphs that are substantially larger than those we have encountered in prior work. For example, the paper contains a drawi ..."
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Cited by 92 (10 self)
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We present a multiscale layout algorithm for the aesthetic drawing of undirected graphs with straightline edges. The algorithm is extremely fast, and is capable of drawing graphs that are substantially larger than those we have encountered in prior work. For example, the paper contains a drawing of a graph with over 15,000 vertices. Also we achieve "nice" drawings of 1000 vertex graphs in about 1 second. The proposed algorithm embodies a new multiscale scheme for drawing graphs, which was motivated by the earlier multiscale algorithm of Hadany and Harel [HH99]. In principle, it could significantly improve the speed of essentially any forcedirected method (regardless of that method's ability of drawing weighted graphs or the continuity of its costfunction).
ACE: A Fast Multiscale Eigenvector Computation for Drawing Huge Graphs
, 2002
"... We present an extremely fast graph drawing algorithm for very large graphs, which we term ACE (for Algebraic multigrid Computation of Eigenvectors). ACE finds an optimal drawing by minimizing a quadratic energy function due to Hall, using a novel algebraic multigrid technique. The algorithm exhibits ..."
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Cited by 73 (13 self)
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We present an extremely fast graph drawing algorithm for very large graphs, which we term ACE (for Algebraic multigrid Computation of Eigenvectors). ACE finds an optimal drawing by minimizing a quadratic energy function due to Hall, using a novel algebraic multigrid technique. The algorithm exhibits an improvement of something like two orders of magnitude over the fastest algorithms we are aware of; it draws graphs of a million nodes in less than a minute. Moreover, the algorithm can deal with more general entities, such as graphs with masses and negative weights (to be defined in the text), and it appears to be applicable outside of graph drawing too.
GeometryBased edge clustering for graph visualization
 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
, 2008
"... Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a novel geometrybased edgeclustering framework that can group edges into bundles to reduce the ove ..."
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Cited by 63 (4 self)
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Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a novel geometrybased edgeclustering framework that can group edges into bundles to reduce the overall edge crossings. Our method uses a control mesh to guide the edgeclustering process; edge bundles can be formed by forcing all edges to pass through some control points on the mesh. The control mesh can be generated at different levels of detail either manually or automatically based on underlying graph patterns. Users can further interact with the edgeclustering results through several advanced visualization techniques such as color and opacity enhancement. Compared with other edgeclustering methods, our approach is intuitive, flexible, and efficient. The experiments on some large graphs demonstrate the effectiveness of our method.
Tools for visually exploring biological networks
 BIOINFORMATICS
, 2007
"... Many tools exist for visually exploring biological networks including wellknown examples such as Cytoscape, VisANT, Pathway Studio and Patika. These systems play a key role in the development of integrative biology, systems biology and integrative bioinformatics. The trend in the development of the ..."
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Cited by 58 (0 self)
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Many tools exist for visually exploring biological networks including wellknown examples such as Cytoscape, VisANT, Pathway Studio and Patika. These systems play a key role in the development of integrative biology, systems biology and integrative bioinformatics. The trend in the development of these tools is to go beyond “static” representations of cellular state, towards a more dynamic model of cellular processes through the incorporation of gene expression data, subcellular localization information, and timedependent behavior. We provide a comprehensive review of the relative advantages and disadvantages of existing systems with two goals in mind: to aid researchers in efficiently identifying the appropriate existing tools for data visualization; to describe the necessary and realistic goals for the next generation of visualization tools. In view of the first goal, we provide in the Supplementary Materials a systematic comparison of more than 35 existing tools in terms of over 25 different features.
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 58 (6 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.
An Experimental Comparison of ForceDirected and Randomized Graph Drawing Algorithms
, 1996
"... . We report on our experiments with five graph drawing algorithms for general undirected graphs. These are the algorithms FR introduced by Fruchterman and Reingold [5], KK by Kamada and Kawai [11], DH by Davidson and Harel [1], Tu by Tunkelang [13] and GEM by Frick, Ludwig and Mehldau [6]. Implement ..."
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Cited by 54 (1 self)
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. We report on our experiments with five graph drawing algorithms for general undirected graphs. These are the algorithms FR introduced by Fruchterman and Reingold [5], KK by Kamada and Kawai [11], DH by Davidson and Harel [1], Tu by Tunkelang [13] and GEM by Frick, Ludwig and Mehldau [6]. Implementations of these algorithms have been integrated into our GraphEd system [9]. We have tested these algorithms on a wide collection of examples and with different settings of parameters. Our examples are from original papers and by our own. The obtained drawings are evaluated both empirically and by GraphEd's evaluation toolkit. As a conclusion we can confirm the reported good behaviour of the algorithms. Combining time and quality we recommend to use GEM or KK first, then FR and Tu and finally DH. 1 Introduction Graph drawing has become an important area of research in Computer Science. There is a wide range of applications including data structures, data bases, software engineering, VLSI te...
The art and science of dynamic network visualization
 Journal of Social Structure
, 2006
"... If graph drawing is to become a methodological tool instead of an illustrative art, many concerns need to be overcome. We discuss the problems of social network visualization, and particularly, problems of dynamic network visualization. We consider issues that arise from the aggregation of continuou ..."
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Cited by 53 (0 self)
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If graph drawing is to become a methodological tool instead of an illustrative art, many concerns need to be overcome. We discuss the problems of social network visualization, and particularly, problems of dynamic network visualization. We consider issues that arise from the aggregation of continuoustime relational data ("streaming " interactions) into a series of networks. We discuss our experience developing SoNIA (Social Network Image Animator,