  Summarized data analysis of graphs using OLAP (Online Analytical Processing)
is very popular these days. However due to high dimensionality and large size,
it is not easy to decide which data should be aggregated for OLAP analysis.
Though iceberg cubing is useful, but it is unaware of the significance of
dimensional values with respect to the structure of the graph. In this paper,
we propose a Structural Significance, SS, measure to identify the structurally
significant dimensional values in each dimension. This leads to structure aware
pruning. We then propose an algorithm, iGraphCubing, to compute the graph cube
to analyze the structurally significant data using the proposed measure. We
evaluated the proposed ideas on real and synthetic data sets and observed very
encouraging results.
