摘要
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.
摘要译文
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会在网络中看到的链接。所提出的方法将社区结构作为特征纳入网络中丢失链接的预测中。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功能以外，还考虑了社区结构的影响。我们从真实数据集的曲线下（AUC）指标的精度，召回率，准确性和面积方面分析了设计方法的性能。此外，我们针对现实世界和综合数据集的执行时间检查了设计方法的性能。拟议的方法优于其他现有方法，如稍后将通过实验进行展示。
Rahul Kumar Yadav[1];Abhay Kumar Rai[2]. Incorporating communities’ structures in predictions of missing links[J]. Journal of Intelligent Information Systems, 2020,55(1): 183-205