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Community detection in social network using shuffled frog-leaping optimisation

机译:使用随机蛙跳优化的社交网络社区检测

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摘要

In recent years, the complex division of the online community has become a hot topic. The existing community method aims to divide nodes into a community mechanically. In a real network, it will reduce the classification accuracy greatly for the low active users, while increasing the time complexity. It has small significance. Therefore, this paper will combine shuffled leap-frog algorithm with community detection method. It will extract active users by sorting on properties of frog, so as to improve the efficiency of division. Experimental results show that the method has good performance.
机译:近年来,在线社区的复杂划分已成为热门话题。现有的社区方法旨在将节点机械地划分为社区。在真实的网络中,它将为低活跃用户大大降低分类精度,同时增加时间复杂度。它的意义不大。因此,本文将改组跳蛙算法与社区检测方法相结合。它将通过对青蛙的属性进行排序来提取活跃用户,从而提高划分效率。实验结果表明该方法具有良好的性能。

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