Consistent Community Identification in Complex Networks

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Abstract
We have found that known community identification algorithms produce inconsistent communities when the node ordering changes at input. We propose two metrics to quantify the level of consistency across multiple runs of an algorithm: pairwise membership probability and consistency. Based on these two metrics, we address the consistency problem without compromising the modularity. Our solution uses pairwise membership probabilities as link weights and generates consistent communities within six or fewer cycles. It offers a new tool in the study of community structures and their evolutions.

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