Ke Dejing, Pu Jiansu
Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Big Data Visual Analysis Lab, University of Electronic Science and Technology of China, Chengdu 611731, China.
Entropy (Basel). 2023 Oct 5;25(10):1416. doi: 10.3390/e25101416.
Link prediction plays an important role in the research of complex networks. Its task is to predict missing links or possible new links in the future via existing information in the network. In recent years, many powerful link prediction algorithms have emerged, which have good results in prediction accuracy and interpretability. However, the existing research still cannot clearly point out the relationship between the characteristics of the network and the mechanism of link generation, and the predictability of complex networks with different features remains to be further analyzed. In view of this, this article proposes the corresponding link prediction indexes Reg, DFPA and LW on a regular network, scale-free network and small-world network, respectively, and studies their prediction properties on these three network models. At the same time, we propose a parametric hybrid index HEM and compare the prediction accuracies of HEM and many similarity-based indexes on real-world networks. The experimental results show that HEM performs better than other Birnbaum-Saunders. In addition, we study the factors that play a major role in the prediction of HEM and analyze their relationship with the characteristics of real-world networks. The results show that the predictive properties of factors are closely related to the features of networks.
链路预测在复杂网络研究中起着重要作用。其任务是通过网络中的现有信息预测未来缺失的链路或可能的新链路。近年来，涌现出许多强大的链路预测算法，它们在预测准确性和可解释性方面都有不错的效果。然而，现有研究仍无法明确指出网络特征与链路生成机制之间的关系，不同特征复杂网络的可预测性仍有待进一步分析。鉴于此，本文分别在规则网络、无标度网络和小世界网络上提出了相应的链路预测指标Reg、DFPA和LW，并研究它们在这三种网络模型上的预测特性。同时，我们提出了一个参数化混合指标HEM，并在真实网络上比较了HEM与许多基于相似度指标的预测准确性。实验结果表明，HEM的表现优于其他指标。此外，我们研究了在HEM预测中起主要作用的因素，并分析了它们与真实网络特征的关系。结果表明，这些因素的预测特性与网络特征密切相关。
