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80
Graph Visualization and Navigation in Information Visualization: a Survey
 IEEE Transactions on Visualization and Computer Graphics
, 2000
"... This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as web browsing, statetransition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs ..."
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Cited by 322 (3 self)
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This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as web browsing, statetransition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective. Index TermsInformation visualization, graph visualization, graph drawing, navigation, focus+context, fisheye, clustering. 1
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 95 (1 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
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 80 (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 63 (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.
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 44 (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...
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 37 (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.
A Multidimensional Approach to ForceDirected Layouts of Large Graphs
, 2000
"... Abstract. We present a novel hierarchical forcedirected method for drawing large graphs. The algorithm produces a graph embedding in an Euclidean space E of any dimension. A two or three dimensional drawing of the graph is then obtained by projecting a higherdimensional embedding into a two or thr ..."
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Cited by 36 (5 self)
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Abstract. We present a novel hierarchical forcedirected method for drawing large graphs. The algorithm produces a graph embedding in an Euclidean space E of any dimension. A two or three dimensional drawing of the graph is then obtained by projecting a higherdimensional embedding into a two or three dimensional subspace of E. Projecting highdimensional drawings onto two or three dimensions often results in drawings that are “smoother ” and more symmetric. Among the other notable features of our approach are the utilization of a maximal independent set filtration of the set of vertices of a graph, a fast energy function minimization strategy, efficient memory management, and an intelligent initial placement of vertices. Our implementation of the algorithm can draw graphs with tens of thousands of vertices using a negligible amount of memory in less than one minute on a midrange PC. 1
GRIP: Graph dRawing with Intelligent Placement  Short System Demonstration
"... This paper describes a system for Graph dRawing with Intelligent Placement, GRIP. The GRIP system is designed for drawing large graphs and uses a novel multidimensional forcedirected method together with fast energy function minimization. The system allows for drawing graphs with tens of thousa ..."
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Cited by 34 (7 self)
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This paper describes a system for Graph dRawing with Intelligent Placement, GRIP. The GRIP system is designed for drawing large graphs and uses a novel multidimensional forcedirected method together with fast energy function minimization. The system allows for drawing graphs with tens of thousands of vertices in under a minute on a midrage PC. To the best of the authors' knowledge GRIP surpasses the fastest previous algorithms. However, speed is not achieved at the expense of quality as the resulting drawings are quite aesthetically pleasing.
Communicating Centrality in Policy Network Drawings
, 2003
"... We introduce a network visualization technique that supports an analytical method applied in the social sciences. Policy network analysis is an approach to study policy making structures, processes, and outcomes, thereby concentrating on relations between policy actors. An important operational co ..."
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Cited by 34 (11 self)
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We introduce a network visualization technique that supports an analytical method applied in the social sciences. Policy network analysis is an approach to study policy making structures, processes, and outcomes, thereby concentrating on relations between policy actors. An important operational concept for the analysis of policy networks is the notion of centrality, i.e., the distinction of actors according to their importance in a relational structure. We integrate this measure in a layout model for networks by mapping structural to geometric centrality. Thus, centrality values and network data can be presented simultaneously and explored interactively.
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 34 (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.