Abstract
Emerging applications face the need to store and analyze interconnected data that are naturally depicted as graphs. Recent proposals take the idea of data cubes that have been successfully applied to multidimensional data and extend them to work for interconnected datasets. In our work we revisit the graph cube framework and propose novel mechanisms inspired from information theory in order to help the analyst quickly locate interesting relationships within the rich information contained in the graph cube. The proposed entropy-based filtering of data reveals irregularities and non-uniformity which are often what the decision maker is looking for. We experimentally validate our techniques and demonstrate that the proposed entropy-based filtering can help eliminate large portions of the respective graph cubes.
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