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An advanced automated approach for community mining in signed social networks

机译：签名社交网络中用于社区挖掘的高级自动化方法

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A number of recent studies have focused on the properties of social networks. In this article, we highlight the property of community structure, in which the nodes of the network are joined together in tightly knit groups and there are looser connections between the groups. We propose an approach AACMA ( Advanced Automatic community Mining Approach ) for detecting such communities, build around the idea of using centrality measure to find community boundaries. We test AACMA on real-world graphs whose community is already detected and it detects this known structure with high sensitivity and reliability. We also compare the method with a different method named ABCD (Attractiveness-Based Community Detection) by using the same dataset. We find that AACMA provides more accurate results than the compared approach.
机译：最近的许多研究集中在社交网络的属性上。在本文中，我们强调了社区结构的属性，其中网络的节点以紧密编织的组连接在一起，并且组之间的连接较松散。我们提出了一种方法AACMA（高级自动社区挖掘方法）来检测此类社区，并围绕使用集中度度量来查找社区边界的想法进行构建。我们在已经检测到社区的现实世界图上测试AACMA，并且它以高灵敏度和可靠性检测到这种已知结构。我们还使用相同的数据集，将该方法与名为ABCD（基于吸引力的社区检测）的其他方法进行了比较。我们发现AACMA提供了比比较方法更准确的结果。

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