Consistent Community Identi¯cation in Complex Networks

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Abstract
We have found that known community identification algorithmsproduce inconsistent communities when the node ordering changes atinput. We propose two metrics to quantify the level of consistencyacross multiple runs of an algorithm: pairwise membershipprobability and consistency. Based on these two metrics, weaddress the consistency problem without compromising themodularity. Our solution uses pairwise membership probabilitiesas link weights and generates consistent communities within six orfewer cycles. It offers a new tool in the study of communitystructures and their evolutions

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