Matt Stabeler

Comparing BubbleH and Bubble RAP for the Enron dataset produced the following results. The plot shows the results for the best run of each parameter, best is determined, in this case, by delivery ratio. i.e. I selected the run with the highest delivery ratio for each CFA and each routing type, and used these as the basis for the plot. This was done automatically, and this initial version does not take into account secondary ordering – meaning that in a run with two identical best delivery ratios, the first to be encountered is picked, ignoring secondary data such as cost or latentcy.

Delivery Ratio, Cost, Latency and Delivered Hops for InfoMap, HGCE, LinkClustering and KCLIQUE, for both BubbleRAP and BubbleH

WE can see that in terms of Delivery Ratio, BubbleH outperforms BubbleRAP when there is overlap (LinkClustering, HGCE) and when there is Hierarchical Data, it performs better (HGCE). When there is little or no overlap, BubbleH and BubbleRAP perform identically, as we know/expect. I wonder if we can explicitly test the effect of Hierarchy  by finding an algorithm that partitions into hierarchy (i.e. without overlap)?

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Next to do: Test Hypothesis – Does HGCE deep Hierarchy beat HGCE flat HEIRARCHY.

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The table above shows the statistics for Enron, HGCE, BubbleH,  for DATASET_EXPLORE2 which is a new run using the original, simple datasets (without multiple runs over concatenated datasets). it still needs depth,width data adding.