Supervised link prediction in multiplex networks
Supervised link prediction in multiplex networks
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DOI：
10.1016/j.knosys.2020.106168
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发表时间：
2020-09-05
影响因子：
8.8
通讯作者：
Chen, Xiaoyun
中科院分区：
文献类型：
--
作者：
Shan, Na;Li, Longjie;Chen, Xiaoyun
近年来，多路网络被引入来描述真实的复杂系统，其中同一组实体进行不同类型的交互。在多路网络中，每一层表示一种不同类型的交互。链路预测是复杂网络分析中的一个研究热点。目前已经提出了大量的链路预测方法，但针对多路复用网络的链路预测方法很少。本文主要研究多路复用网络中的链路预测问题。我们认为，同时考虑各层信息进行链路预测的方法是可取的，因为一层中链路的形成可能会受到其他层中相同节点对链路的影响。本文提出了一种基于监督的多路网络链路预测方法，该方法将链路预测视为一个二分类问题。在该方法中，通过从所有层中提取节点对的一组精细结构特征来提供分类模型。在六个网络上进行了大量的实验，以分析所提出方法的有效性。结果表明，该方法的性能明显优于其他方法。(C) 2020 Elsevier B.V.版权所有
In recent years, multiplex networks have been introduced to describe real complex systems, where the same group of entities make different types of interaction. In a multiplex network, each layer expresses one distinct type of interaction. Link prediction is a research hotspot in complex network analysis. A large number of link prediction methods have been proposed, but only a few were designed for multiplex networks. In this paper, we focus on the link prediction problem in multiplex networks. In our opinion, an approach in which link prediction is performed by simultaneously considering the information from all layers is advisable, because the formation of links in one layer can be affected by links of the same node pairs in other layers. A supervised method is proposed in this study to implement link prediction in multiplex networks, which regards link prediction as a binary classification problem. In the proposed method, a classification model is fed by a set of elaborate structural features of node pairs that are extracted from all layers. Extensive experiments are conducted on six networks to analyze the effectiveness of the proposed method. The results demonstrate that the proposed method outperforms the compared methods significantly. (C) 2020 Elsevier B.V. All rights reserved.