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A novel overlapping community detection algorithm of weighted networks

机译:一种重叠网络重叠网络的重叠群落检测算法

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The community structure is a vital property of complex networks. As special networks the weighted networks also have community structure. Nowadays the studies of overlapping community draw attentions of researchers. However, the scale of networks become huge, so it requires the algorithm has lower time complexity and higher classification accuracy. Many existing algorithms cannot meet these two requirements at the same time. So we propose a novel overlapping community detection algorithm. Firstly we apply maximum degree node and its some special adjacent nodes as the initial community, and then expand the initial community by adding eligible nodes to it, finally other communities can be found by repeating these two steps. Experiments results show that our algorithm can detect overlapping community structure from weighted networks successfully, and also reveal that our method has higher division accuracy and lower time complexity than many previously proposed methods.
机译:社区结构是复杂网络的重要属性。作为特殊网络,加权网络也具有社区结构。如今,重叠社区的研究引起了研究人员的注意。但是,网络的规模变得巨大,因此需要该算法具有较低的时间复杂度和更高的分类精度。许多现有算法同时不能满足这两个要求。因此,我们提出了一种新型重叠的社区检测算法。首先,我们将最大程度的节点及其一些特殊的相邻节点应用为初始社区,然后通过将符合条件的节点添加到它来扩展初始社区,最后可以通过重复这两个步骤来找到其他社区。实验结果表明,我们的算法可以成功地检测从加权网络的重叠群落结构,并且还揭示了我们的方法具有更高的分割精度和较低的时间复杂性,而不是许多先前提出的方法。

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