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Community Detection in Social Networks Based on Influential Nodes

机译:基于影响节点的社交网络社区检测

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Large-scale social networks emerged rapidly in recent years. Social networks have become complex networks. The structure of social networks is an important research area and has attracted much scientific interest. Community is an important structure in social networks. In this paper, we propose a community detection algorithm based on influential nodes. First, we introduce how to find influential nodes based on random walk. Then we combine the algorithm with order statistics theory to find community structure. We apply our algorithm in three classical data sets and compare to other algorithms. Our community detection algorithm is proved to be effective in the experiments. Our algorithm also has applications in data mining and recommendations.
机译:近年来,大规模的社交网络迅速兴起。社交网络已成为复杂的网络。社交网络的结构是重要的研究领域,引起了很多科学兴趣。社区是社交网络中的重要结构。本文提出了一种基于影响节点的社区检测算法。首先,我们介绍如何基于随机游走找到有影响力的节点。然后,将算法与顺序统计理论相结合,找到社区结构。我们将算法应用于三个经典数据集,并与其他算法进行比较。实验证明,我们的社区检测算法是有效的。我们的算法在数据挖掘和推荐中也有应用。

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