Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.
College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
Sci Rep. 2020 Nov 16;10(1):19848. doi: 10.1038/s41598-020-76860-2.
Link prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.
在复杂网络中，链路预测是网络科学中一个非常有趣的问题，近年来受到了越来越多的关注。它旨在预测复杂系统中两个实体之间尚未连接的缺失（或未来）链路。在现有的方法中，局部相似性指数是最受欢迎的，它考虑了共同邻居的信息来估计两个节点之间连接的可能性。在本文中，我们提出了一些常用局部相似性指数的全局和准局部扩展。我们在来自不同领域的公开可用数据集上进行了广泛的数值模拟，结果表明，与各自的局部指数相比，所提出的扩展不仅具有更好的性能，而且还优于一些当前的、最先进的局部和全局链路预测方法。
