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首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Consensus-based methodology for detection communities in multilayered networks
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Consensus-based methodology for detection communities in multilayered networks

机译：基于共识的多层网络中的检测社区的方法

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AbstractFinding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users’ behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.Highlights?
机译：<！[cdata [ Abstract 查找与彼此密集相关的网络用户组出现在社交网络分析领域的一个有趣问题。这些群体或所谓的社区将隐藏在用户的行为背后。大多数研究假设通过专注于用户界面，其行为属性或这些网络层的组合（即，与其属性的接口）来理解这种行为。他们还假设所有网络层都指的是相同的行为。然而，在现实生活网络中，一层中的用户的行为可能与另一层中的行为不同。为了应对这些问题，本文提出了一种基于共识的社区检测方法（CBC）。 CBC并行地在每个图层的节点中找到社区。然后，应使用共识聚类方法汇聚图层的结果。这意味着可以在分析中检测和使用不同的行为。至于其他显着优势，方法能够处理缺失的值。已经进行了三个关于现实生活和计算机生成的数据集的实验，以评估CBC的性能。结果表明，与其他方法相比，CBC的优越性和稳定性。 突出显示 ？

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