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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020-05-08
摘要
The most widely used techniques for community detection in 網絡s, including methods based on modularity, 統計 inference, and 資訊 theoretic arguments, all work by optimizing objective functions that measure the quality of 網絡 partitions. There is a good case to be made, however, that one should not look solely at the single optimal community structure under such an objective function but rather at a selection of high-scoring structures. If one does this, one typically finds that the resulting structures show considerable variation, which could be taken as evidence that these community detection methods are unreliable, since they do not appear to give consistent answers. Here we argue that, upon closer inspection, the structures found are in fact consistent in a certain way. Specifically, we show
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