Department of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
School of Engineering, RMIT University, Melbourne, Australia.
Sci Rep. 2019 Aug 30;9(1):12604. doi: 10.1038/s41598-019-49001-7.
Recently multilayer networks are introduced to model real systems. In these models the individuals make connection in multiple layers. Transportation networks, biological systems and social networks are some examples of multilayer networks. There are various link prediction algorithms for single-layer networks and some of them have been recently extended to multilayer networks. In this manuscript, we propose a new link prediction algorithm for multiplex networks using two novel similarity metrics based on the hyperbolic distance of node pairs. We use the proposed methods to predict spurious and missing links in multiplex networks. Missing links are those links that may appear in the future evolution of the network, while spurious links are the existing connections that are unlikely to appear if the network is evolving normally. One may interpret spurious links as abnormal links in the network. We apply the proposed algorithm on real-world multiplex networks and the numerical simulations reveal its superiority than the state-of-the-art algorithms.
最近，多层网络被引入到模型中来描述真实系统。在这些模型中，个体在多个层中建立连接。交通网络、生物系统和社交网络就是多层网络的一些例子。对于单层网络，有各种链接预测算法，其中一些已经最近被扩展到多层网络中。在本文中，我们提出了一种新的使用基于节点对双曲距离的两个新的相似性度量的多重网络链路预测算法。我们使用所提出的方法来预测多重网络中的虚假和缺失链接。缺失链接是指那些可能出现在网络未来演化中的链接，而虚假链接是指如果网络正常演化，不太可能出现的现有连接。人们可以将虚假链接解释为网络中的异常链接。我们将所提出的算法应用于真实的多重网络，数值模拟表明它比现有的算法具有优越性。
