Community detection by fuzzy clustering
Community detection by fuzzy clustering
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DOI：
10.1016/j.physa.2014.10.009
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发表时间：
2015-02
影响因子：
3.3
通讯作者：
P. Sun
中科院分区：
文献类型：
--
作者：
P. Sun
当我们使用模糊聚类方法来发现复杂网络中的社区时，如何衡量节点之间的相似性对于模糊聚类非常重要。在本文中，我们首先基于边缘中心性测量网络中节点之间的相似性，并将网络建模为模糊关系。然后，分别对关系应用两个模糊传递规则（规则一和规则二），通过规则可以将相似性信息从网络中的一个节点传递到另一个节点，直到关系达到稳定状态。通过选择不同的阈值，我们的方法最终可以将网络划分为几个不重叠的子组。我们将我们的方法与 LFR 基准和现实网络上的一些最先进的方法进行比较。我们发现，当相同组的节点之间的相似度大于不同组的节点之间的相似度时，基于规则 I 的方法可以正确识别社区，而与规则 II 正好相反。当随机网络的预先植入的社区更加模糊时，我们的方法比最先进的方法取得了更好的结果。
How to measure the similarity between nodes is of great importance for fuzzy clustering when we use the approach to uncover communities in complex networks. In this paper, we first measure the similarity between nodes in a network based on edge centralities and model the network as a fuzzy relation. Then, two fuzzy transitive rules (Rule I and Rule II) are applied on the relation respectively, by which the similarity information can be transferred from one node to another in the network until the relation reaches a stable state. By choosing different thresholds, our method finally can partition the network into several non-overlapping subgroups. We compare our method with some state of the art methods on the LFR benchmark and real-world networks. We find that our method based on Rule I can correctly identify communities when the similarity between nodes of same groups is greater than that of different groups, while it is just opposite to Rule II. Our method achieves better results than the state of the art methods when the pre-planted communities of the random networks are vaguer.