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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 50 (9 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
 IEEE Transactions on Visualization and Computer Graphics
, 2003
"... ..."
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 41 (6 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
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 40 (8 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.
Graph Drawing in Motion
 Journal of Graph Algorithms and Applications
, 2002
"... Enabling the user of a graph drawing system to preserve the mental map between two di#erent layouts of a graph is a major problem. In this paper we present methods that smoothly transform one drawing of a graph into another without any restrictions to the class of graphs or type of layout algori ..."
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Cited by 37 (0 self)
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Enabling the user of a graph drawing system to preserve the mental map between two di#erent layouts of a graph is a major problem. In this paper we present methods that smoothly transform one drawing of a graph into another without any restrictions to the class of graphs or type of layout algorithm.
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 37 (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.
Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context
, 2008
"... Abstract — Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists observe the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and ..."
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Cited by 34 (5 self)
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Abstract — Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists observe the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and then propose changes to the graph model. These graphs serve as a form of dynamic knowledge representation of the biological system being studied and evolve as new insight is gained from the experimental data. While numerous graph layout and drawing packages are available, these tools did not fully meet the needs of our immunologist collaborators. In this paper, we describe the data information display needs of these immunologists and translate them into design decisions. These decisions led us to create Cerebral, a system that uses a biologically guided graph layout and incorporates experimental data directly into the graph display. Small multiple views of different experimental conditions and a datadriven parallel coordinates view enable correlations between experimental conditions to be analyzed at the same time that the data is viewed in the graph context. This combination of coordinated views allows the biologist to view the data from many different perspectives simultaneously. To illustrate the typical analysis tasks performed, we analyze two datasets using Cerebral. Based on feedback from our collaborators we conclude that Cerebral is a valuable tool for analyzing experimental data in the context of an interaction graph model. Index Terms—Graph layout, systems biology visualization, small multiples, design study. 1
Metricsbased 3d visualization of large objectoriented programs
, 2002
"... Software belongs to the most complex humanmade artefacts. The size and complexity of programs has constantly grown over the last years. Today in many application domains (e.g. ebusiness, switching systems) software systems with millions of lines of code are constructed. They consist of many thousa ..."
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Cited by 31 (4 self)
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Software belongs to the most complex humanmade artefacts. The size and complexity of programs has constantly grown over the last years. Today in many application domains (e.g. ebusiness, switching systems) software systems with millions of lines of code are constructed. They consist of many thousands
A Fast MultiDimensional Algorithm for Drawing Large Graphs
 In Graph Drawing’00 Conference Proceedings
, 2000
"... 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 dimensi ..."
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Cited by 30 (5 self)
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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, e#cient 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 Introduction Graphs are common in many applications, from data structures to networks, from software engineering...