Abstract
Numerous information visualization techniques are available for utilizing and analyzing big data. Among which, network visualization that employs node-link diagrams can determine the relationship among multi-dimensional data. However, when data become extremely large, visualization becomes obscure because of visual clutter. To address this problem, many edge bundling techniques have been proposed. However, although graphs have several attributions, previous techniques do not reflect these attributions. In this paper, we propose a new edge bundling method for attributed co-occurrence graphs. Electrostatic forces work between each pair of edges; however, if the edges are under different attributions, then repulsion works between pairs. By bundling edges under the same attribution, a graph can more clearly show the relationships among data.
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