Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

中文翻译：

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基于聚类方法的未来贪婪方法社区检测广告

社交网络中的社区检测是电子营销中的广告方法之一。在大型社交网络中寻找社区的方法之一是使用贪心方法，因为这些方法执行速度非常快。贪心方法一般是根据局部决策设计的；因此，不恰当的局部决策可能导致不恰当的全局解决方案。使用具有未来主义方法的贪婪改进指数可以在某种程度上防止不适当的本地选择。我们提出的方法基于追随者和追随者以及新的未来主义贪婪指数来确定社交网络中的有影响力的节点。它通过具有新距离函数的基于密度的聚类算法根据有影响的节点对节点进行分类。所提出的方法可以通过未来贪婪方法提高聚类精度以检测社区。我们在 Hadoop 结构中使用 map-reduce 技术实现了所提出的算法。在数据集中的实验结果表明，所提出的方法实现了99.32%的聚类的rand指数的平均值。此外，这些结果表明，所提出的算法减少了执行时间。