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30
MatrixExplorer: a DualRepresentation System to Explore Social Networks
 IEEE Transactions on Visualization and Computer Graphics
, 2006
"... Abstract — MatrixExplorer is a network visualization system that uses two representations: nodelink diagrams and matrices. Its design comes from a list of requirements formalized after several interviews and a participatory design session conducted with social science researchers. Although matrices ..."
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Cited by 92 (13 self)
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Abstract — MatrixExplorer is a network visualization system that uses two representations: nodelink diagrams and matrices. Its design comes from a list of requirements formalized after several interviews and a participatory design session conducted with social science researchers. Although matrices are commonly used in social networks analysis, very few systems support the matrixbased representations to visualize and analyze networks. MatrixExplorer provides several novel features to support the exploration of social networks with a matrixbased representation, in addition to the standard interactive filtering and clustering functions. It provides tools to reorder (layout) matrices, to annotate and compare findings across different layouts and find consensus among several clusterings. MatrixExplorer also supports Nodelink diagram views which are familiar to most users and remain a convenient way to publish or communicate exploration results. Matrix and nodelink representations are kept synchronized at all stages of the exploration process. Index Terms — social networks visualization, nodelink diagrams, matrixbased representations, exploratory process, matrix ordering, interactive clustering, consensus. Fig. 1. MatrixExplorer showing two synchronized representations of the same network: matrix on the left and nodelink on the right. 1
Geometric and Combinatorial Tiles in 01 Data
 In: Proceedings PKDD’04. Volume 3202 of LNAI
, 2004
"... In this paper we introduce a simple probabilistic model, hierarchical tiles, for 01 data. A basic tile (X,Y,p) specifies a subset X of the rows and a subset Y of the columns of the data, i.e., a rectangle, and gives a probability p for the occurrence of 1s in the cells of X x Y. A hierarchical tile ..."
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Cited by 22 (0 self)
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In this paper we introduce a simple probabilistic model, hierarchical tiles, for 01 data. A basic tile (X,Y,p) specifies a subset X of the rows and a subset Y of the columns of the data, i.e., a rectangle, and gives a probability p for the occurrence of 1s in the cells of X x Y. A hierarchical tile has additionally a set of exception tiles that specify the probabilities for subrectangles of the original rectangle. If the rows and columns are ordered and X and Y consist of consecutive elements in those orderings, then the tile is geometric; otherwise it is combinatorial. We give a simple randomized algorithm for finding good geometric tiles. Our main result shows that using spectral ordering techniques one can find good orderings that turn combinatorial tiles into geometric tiles. We give empirical results on the performance of the methods.
Mesh layouts for blockbased caches
 IEEE Transactions on Visualization and Computer Graphics
, 2006
"... Abstract—Current computer architectures employ caching to improve the performance of a wide variety of applications. One of the main characteristics of such cache schemes is the use of block fetching whenever an uncached data element is accessed. To maximize the benefit of the block fetching mechani ..."
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Cited by 21 (8 self)
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Abstract—Current computer architectures employ caching to improve the performance of a wide variety of applications. One of the main characteristics of such cache schemes is the use of block fetching whenever an uncached data element is accessed. To maximize the benefit of the block fetching mechanism, we present novel cacheaware and cacheoblivious layouts of surface and volume meshes that improve the performance of interactive visualization and geometric processing algorithms. Based on a general I/O model, we derive new cacheaware and cacheoblivious metrics that have high correlations with the number of cache misses when accessing a mesh. In addition to guiding the layout process, our metrics can be used to quantify the quality of a layout, e.g. for comparing different layouts of the same mesh and for determining whether a given layout is amenable to significant improvement. We show that layouts of unstructured meshes optimized for our metrics result in improvements over conventional layouts in the performance of visualization applications such as isosurface extraction and viewdependent rendering. Moreover, we improve upon recent cacheoblivious mesh layouts in terms of performance, applicability, and accuracy. Index Terms—Mesh and graph layouts, cacheaware and cacheoblivious layouts, metrics for cache coherence, data locality. 1
Graph Minimum Linear Arrangement by Multilevel Weighted Edge Contractions
, 2006
"... The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. In this paper we present a lineartime algorithm for the problem inspired by the algebraic multigrid approach which is based on weighted edge contraction rather than simple contraction. ..."
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Cited by 20 (7 self)
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The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. In this paper we present a lineartime algorithm for the problem inspired by the algebraic multigrid approach which is based on weighted edge contraction rather than simple contraction. Our results turned out to be better than every known result in almost all cases, while the short running time of the algorithm enabled experiments with very large graphs.
Fragments of Order
, 2003
"... Highdimensional collections of 01 data occur in many applications. The attributes in such data sets are typically considered to be unordered. However, in many cases there is a natural total or partial order # underlying the variables of the data set. Examples of variables for which such orders exi ..."
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Cited by 13 (2 self)
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Highdimensional collections of 01 data occur in many applications. The attributes in such data sets are typically considered to be unordered. However, in many cases there is a natural total or partial order # underlying the variables of the data set. Examples of variables for which such orders exist include terms in documents, courses in enrollment data, and paleontological sites in fossil data collections. The observations in such applications are flat, unordered sets; however, the data sets respect the underlying ordering of the variables. By this we mean that if A # B # C are three variables respecting the underlying ordering #, and both of variables A and C appear in an observation, then, up to noise levels, variable B also appears in this observation. Similarly, if A1 # A2 # # A l1 # A l is a longer sequence of variables, we do not expect to see many observations for which there are indices i < j < k such that A i and Ak occur in the observation but A j does not.
A TwoWay Visualization Method for Clustered Data
, 2003
"... We describe a novel approach to the visualization of hierarchical clustering that superimposes the classical dendrogram over a fully synchronized lowdimensional embedding, thereby gaining the benefits of both approaches. In a single image one can view all the clusters, examine the relations between ..."
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Cited by 13 (3 self)
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We describe a novel approach to the visualization of hierarchical clustering that superimposes the classical dendrogram over a fully synchronized lowdimensional embedding, thereby gaining the benefits of both approaches. In a single image one can view all the clusters, examine the relations between them and study many of their properties. The method is based on an algorithm for lowdimensional embedding of clustered data, with the property that separation between all clusters is guaranteed, regardless of their nature. In particular, the algorithm was designed to produce embeddings that strictly adhere to a given hierarchical clustering of the data, so that every two disjoint clusters in the hierarchy are drawn separately.
Multilevel algorithms for linear ordering problems
, 2007
"... Linear ordering problems are combinatorial optimization problems which deal with the minimization of different functionals in which the graph vertices are mapped onto (1, 2,..., n). These problems are widely used and studied in many practical and theoretical applications. In this paper we present a ..."
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Cited by 12 (7 self)
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Linear ordering problems are combinatorial optimization problems which deal with the minimization of different functionals in which the graph vertices are mapped onto (1, 2,..., n). These problems are widely used and studied in many practical and theoretical applications. In this paper we present a variety of lineartime algorithms for these problems inspired by the Algebraic Multigrid approach which is based on weighted edge contraction. The experimental result for four such problems turned out to be better than every known result in almost all cases, while the short running time of the algorithms enables testing very large graphs.
Drawing Directed Graphs Using OneDimensional Optimization
 Proc. Graph Drawing 2002, LNCS 2528
, 2001
"... We present an algorithm for drawing directed graphs, which is based on rapidly solving a unique onedimensional optimization problem for each of the axes. The algorithm results in a clear description of the hierarchy structure of the graph. Nodes are not restricted to lie on fixed horizontal laye ..."
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Cited by 10 (6 self)
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We present an algorithm for drawing directed graphs, which is based on rapidly solving a unique onedimensional optimization problem for each of the axes. The algorithm results in a clear description of the hierarchy structure of the graph. Nodes are not restricted to lie on fixed horizontal layers, resulting in layouts that convey the symmetries of the graph very naturally. The algorithm can be applied without change to cyclic or acyclic digraphs, and even to graphs containing both directed and undirected edges. We also derive a hierarchy index from the input digraph, which quantitatively measures its amount of hierarchy.
Clustering, visualizing, and navigating for large dynamic graphs
 In Graph Drawing
, 2013
"... Abstract. In this paper, we present a new approach to exploring dynamic graphs. We first propose a new clustering algorithm for dynamic graphs which finds an ideal clustering for each timestep and links the clusters together. The resulting timevarying clusters are then used to define two visual ..."
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Cited by 9 (2 self)
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Abstract. In this paper, we present a new approach to exploring dynamic graphs. We first propose a new clustering algorithm for dynamic graphs which finds an ideal clustering for each timestep and links the clusters together. The resulting timevarying clusters are then used to define two visual representations. The first view is an overview that shows how clusters evolve over time and provides an interface to find and select interesting timesteps. The second view consists of a node link diagram of a selected timestep which uses the clustering to efficiently define the layout. By using the timedependant clustering, we ensure the stability of our visualization and preserve user mental map by minimizing node motion, while simultaneously producing an ideal layout for each time step. Also, as the clustering is computed ahead of time, the second view updates in linear time which allows for interactivity even for graphs with upwards of tens of thousands of nodes. 1
A Multilevel Algorithm for the Minimum 2sum Problem
"... In this paper we introduce a direct motivation for solving the minimum 2sum problem, for which we present a lineartime algorithm inspired by the Algebraic Multigrid approach which is based on weighted edge contraction. Our results turned out to be better than previous results, while the short runn ..."
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Cited by 6 (4 self)
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In this paper we introduce a direct motivation for solving the minimum 2sum problem, for which we present a lineartime algorithm inspired by the Algebraic Multigrid approach which is based on weighted edge contraction. Our results turned out to be better than previous results, while the short running time of the algorithm enabled experiments with very large graphs. We thus introduce a new benchmark for the minimum 2sum problem which contains 66 graphs of various characteristics. In addition, we propose the straightforward use of a part of our algorithm as a powerful local reordering method for any other (than multilevel) framework.