These unbalanced results are due to a feedback effect through which larger clusters tend to grow even larger. Hence the cluster that grows most in the first few iteration, ends up dominating for the remainder of the run. Some seed nodes are more likely to dominate than others, depending on their degree. Seed node 2643 (Numerical Analysis) does not recruit any node into its cluster in every run of the algorithm. Upon examining the edge list. we see this is due to the fact that seed node 2643 actually has a degree of 1.

To lessen the effects of feedback, we introduce changes into the algorithm by giving less weight to joined edges. For two edges of weights and , the edge resulting from merging these two edges has a weight of instead of the standard . This change leads to some better solutions. Min-cut value is reduced to 180,000 with a normalized value of 300.

The problem with this approach though is that, due to the fact that the implementation uses integer weight values, in the first contractions, two edges of weight 1 mergerd into an edge of weight 2 rather than 1.4, thus limiting the effects of the reduced edge weights, which ideally are more important early on in the algorithm.

We also try a variant of the algorithm where edge weights do not accumulate. This constant weight variant performed better than the pervious versions, but only in terms of producing a balanced partition. The resulting partition had more edges in the min cut but produced a better normalized min cut because of the balanced sizes of the clusters.

Algorithm | Min-Cut Value | Normalized Min-Cut |

Randomized Min-Cut | 330906 | 943.5 |

Randomized Min-Cut with Reduced Weights | 183229 | 314.4 |

Randomized Min-Cut with Constant Weights | 684837 | 115.2 |

Ali Salehi 2005-04-29