Matt Stabeler

Our paper for SMW11 was accepted, and now we will be able to include the results from the bug-fixed version of  BubbleH, which performs very well. An addition that Pádraig suggested, was to ignore level information in the hierarchy, and simply use community size. This has the benefit of making the algorithm simpler, but still incorporating hierarchical structure. I ran the two versions side by side and found that they perform almost identically, with some cases ignoring level being slightly better!

MIT-NOV Dataset, with levels included and not included for multiple parameters to H-GCE

This plot is very hard to read, but it is possible to see the similarities at a broad level. The best performing run was with H-GCE parameters of K=3, E=0.15, ST=0.9, MAP=0.9 and Z=2. It’s structure seems to be relatively flat, with a large number of communities:

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This gives us the following graphic for the SMW11 paper.

MIT-NOV Dataset, for multiple parameters to H-GCE, compared to Bubble, PROPHET and Flood

The latency is shown below, which seems to follow the same trend as the previous version, but with BubbleH actually beating all but Flood at the end.

Latency in MIT-NOV dataset for Bubble, BubbleH, PROPHET and Flood.

This is a positive result, but whilst doing some work towards the next paper for NGMAST11, I realised that we should be doing runs for multiple parameters to K-Clique, however for this paper, probably don’t need to worry so much. Also, for reference, the average value for BubbleH is included in the plot below.

We need to consider whether to evaluate multiple parameters to BubbleRAP, and see whether this affects the algorithm. Also, we need to consider whether the hierarchy is really making things better or is it the sheer number of communities, because the best performing run has a quite flat structure.