This article introduces a community-based approach to link prediction that identifies the links likely to be seen in the near future in a network. The proposed method incorporates community structure as a feature in the predictions of missing links in a network. We design a feature-based similarity measure that considers the impact of community structure in addition to other network features in link prediction. We analyze the performance of the devised approach in terms of precision, recall, accuracy, and area-under-the-curve (AUC) metrics on real-world datasets. Further, we examine the performance of the devised method in terms of execution time against real-world and synthetic datasets. The proposed approach outperforms the other existing approaches, as will be shown experimentally later.

中文翻译：

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将社区结构纳入缺失链接的预测

本文介绍了一种基于社区的链接预测方法，该方法可识别在不久的将来可能在网络中看到的链接。所提出的方法将社区结构作为预测网络中缺失链接的一个特征。我们设计了一种基于特征的相似性度量，除了链接预测中的其他网络特征外，还考虑了社区结构的影响。我们分析了所设计方法在真实世界数据集上的精度、召回率、准确度和曲线下面积 (AUC) 指标的性能。此外，我们在针对真实世界和合成数据集的执行时间方面检查了所设计方法的性能。所提出的方法优于其他现有方法，稍后将通过实验证明。