Graph Visualization with Latent Variable Models

978-952-248-095-8 07/2010; DOI: 10.1145/1830252.1830265
Source: OAI

ABSTRACT Large graph layout design by choosing locations for the vertices on the plane, such that the drawn set of edges is understandable, is a tough problem. The goal is ill-defined and usually both optimization and evaluation criteria are only very indirectly related to the goal. We suggest a new and surprisingly effective visualization principle: Position nodes such that nearby nodes have similar link distributions. Since their edges are similar by definition, the edges will become visually bundled and do not interfere. For the definition of similarity we use latent variable models which incorporate the user's assumption of what is important in the graph, and given the similarity construct the visualization with a suitable nonlinear projection method capable of maximizing the precision of the display. We finally show that the method outperforms alternative graph visualization methods empirically, and that at least in the special case of clustered data the method is able to properly abstract and visualize the links. TKK reports in information and computer science, ISSN 1797-5042; 20

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Available from: Juuso A. Parkkinen, Sep 29, 2015
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    • "The groups could be learned from the network topology by community detection [27] [31] [33] and then incorporated in the layout via the proposed group regularization. This approach of integrating mining methods with visualization has also been used in [14] [30] for visualization of groups in static networks . Vice versa, the regularized visualization can also contribute to graph mining; for example, consider the problem of change detection. "
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    ABSTRACT: Dimensionality reduction is one of the basic operations in the toolbox of data analysts and designers of machine learning and pattern recognition systems. Given a large set of measured variables but few observations, an obvious idea is to reduce the degrees of freedom in the measurements by rep resenting them with a smaller set of more "condensed" variables. Another reason for reducing the dimensionality is to reduce computational load in further processing. A third reason is visualization.
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