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Graph Drawing by HighDimensional Embedding
 In GD02, LNCS
, 2002
"... We present a novel approach to the aesthetic drawing of undirected graphs. The method has two phases: first embed the graph in a very high dimension and then project it into the 2D plane using PCA. Experiments we have carried out show the ability of the method to draw graphs of 10 nodes in few seco ..."
Abstract

Cited by 59 (10 self)
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We present a novel approach to the aesthetic drawing of undirected graphs. The method has two phases: first embed the graph in a very high dimension and then project it into the 2D plane using PCA. Experiments we have carried out show the ability of the method to draw graphs of 10 nodes in few seconds. The new method appears to have several advantages over classical methods, including a significantly better running time, a useful inherent capability to exhibit the graph in various dimensions, and an effective means for interactive exploration of large graphs.
Visualizing and Classifying Odors Using a Similarity Matrix
 Proc. 9th International Symposium on Olfaction and Electronic Nose (ISOEN’02), Aracne
, 2002
"... The Lorentzian model is an analytic expression that describes the time response of electronic nose sensors. We show how this model can be utilized to calculate a normal ized similarity index between any two measurements. The set of similarity indices is then used for two purposes: visualization of ..."
Abstract

Cited by 11 (9 self)
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The Lorentzian model is an analytic expression that describes the time response of electronic nose sensors. We show how this model can be utilized to calculate a normal ized similarity index between any two measurements. The set of similarity indices is then used for two purposes: visualization of the data, and classification of new samples. The visualization is carried out using graph drawing tools, and the results are shown to bear some desired properties. The classification is done using a majoritydecision type algorithm, and is demonstrated to have very low error rate. Keywords electronic noses, similarity index, feature extraction, Lorentzian model, graph drawing, visualization, classification
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 9 (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.