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A spreading activation-based label propagation algorithm for overlapping community detection in dynamic social networks

机译：基于扩展激活的标签传播算法，用于动态社交网络中的重叠社区检测

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Community detection in temporal social networks is an increasingly challenging subject in network analysis. The Label Propagation Algorithm (LPA) is a simple and fast approach for community detection in dynamic networks. However, it tends to generate monster communities which decrease the accuracy of community detection, especially in dynamic social networks. In this paper, we propose a modified LPA, called Spreading Activation Label Propagation Algorithm in order to solve the problem. This method assigns a property, called activation value, to each label, where pairs (label name, activation value) are propagated by spreading activation process and the LPA. Furthermore, this algorithm uses two weighting algorithms, where each of them corresponds to one variation of the proposed method. Here, the variations of the proposed method and other available methods on real and synthetic networks are implemented. Experimental results on both real and synthetic networks show that all variations of the proposed method detect communities more accurately compared to the benchmark methods while they are slower than these methods.
机译：临时社交网络中的社区检测是网络分析中越来越具有挑战性的主题。标签传播算法（LPA）是一种用于动态网络中社区检测的简单快速的方法。但是，它倾向于生成怪物社区，这会降低社区检测的准确性，尤其是在动态社交网络中。在本文中，我们提出了一种改进的LPA，称为扩展激活标签传播算法，以解决该问题。此方法为每个标签分配一个称为激活值的属性，其中通过扩展激活过程和LPA传播对（标签名称，激活值）。此外，该算法使用两种加权算法，其中每种加权算法对应于所提出方法的一种变体。在此，实现了所提出的方法和其他在实际和合成网络上可用的方法的变体。在真实和合成网络上的实验结果均表明，与基准方法相比，该方法的所有变体都可以更准确地检测社区，而比基准方法要慢。

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