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原文連結
論文資訊
- 類型：已發表論文
- 日期：2014-12-08
摘要
Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory "communities" in 隨機圖s where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in 網絡s generated by the 隨機 block model. It also performs well on real-world 網絡s, revealing large communities in some 網絡s where previous
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