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Tensor decomposition for link prediction in temporal directed networksThis work was supported by Research Funds for the Central Universities (No. 30918012204).
Journal of Statistical Mechanics: Theory and Experiment ( IF 2.4 ) Pub Date : 2021-02-24 , DOI: 10.1088/1742-5468/abd310
Ting Zhang , Kun Zhang , Laishui Lv , Xun Li , Yue Fang

Link prediction is a challenging research topic that comes along with the prevalence of network data analysis. Compared with traditional link prediction, determining future links in temporal directed networks is more complicated. In this paper, we introduce a novel link prediction method based on non-negative tensor factorization that takes into account the link direction and temporal information. In the proposed method, the temporal directed networks are modeled as a fourth-order tensor, which considers the temporal correlation coefficient of adjacent snapshots. We obtain link information by the factor matrices of tensor decomposition and score node pairs related to the link information. We give the interpretation and prove the convergence of the proposed method. Experiments are conducted on several temporal directed networks. The experimental results show that compared to several well-known link prediction methods, the proposed method improves the performance of link prediction. It is mainly because we use structural and temporal information effectively.



中文翻译:

时间定向网络中用于链接预测的张量分解这项工作得到了中央大学研究基金(No. 30918012204)的支持。

链接预测是一个具有挑战性的研究主题,它伴随着网络数据分析的盛行。与传统的链路预测相比,在时间定向网络中确定将来的链路更为复杂。在本文中,我们介绍了一种基于非负张量分解的新型链接预测方法,该方法考虑了链接方向和时间信息。在所提出的方法中,将时间定向网络建模为四阶张量,该张量考虑了相邻快照的时间相关系数。我们通过张量分解的因子矩阵和与链接信息有关的得分节点对获得链接信息。我们给出了解释并证明了所提出方法的收敛性。实验是在几个时间定向网络上进行的。实验结果表明,与几种著名的链接预测方法相比,该方法提高了链接预测的性能。这主要是因为我们有效地使用了结构和时间信息。

更新日期:2021-02-24
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