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An efficient community detection method based on rank centrality

机译：一种基于等级中心度的高效社区检测方法

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摘要

Community detection is a very important problem in social network analysis. Classical clustering approach, K-means, has been shown to be very efficient to detect communities in networks. However, K-means is quite sensitive to the initial centroids or seeds, especially when it is used to detect communities. To solve this problem, in this study, we propose an efficient algorithm K-rank, which selects the top-K nodes with the highest rank centrality as the initial seeds, and updates these seeds by using an iterative technique like K-means. Then we extend K-rank to partition directed, weighted networks, and to detect overlapping communities. The empirical study on synthetic and real networks show that K-rank is robust and better than the state-of-the-art algorithms including K-means, BGLL, LPA, infomap and OSLOM.
机译：社区检测是社交网络分析中非常重要的问题。事实证明，经典聚类方法K-means非常有效地检测网络中的社区。但是，K均值对初始质心或种子非常敏感，尤其是在用于检测群落时。为了解决这个问题，在本研究中，我们提出了一种有效的算法K-rank，该算法选择具有最高秩中心度的前K个节点作为初始种子，并使用诸如K-means的迭代技术更新这些种子。然后，我们将K-rank扩展到分区导向的加权网络，并检测重叠的社区。对综合网络和真实网络的经验研究表明，K秩比包括K-means，BGLL，LPA，infomap和OSLOM在内的最新算法都强大且更好。

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