Jalili Mahdi, Orouskhani Yasin, Asgari Milad, Alipourfard Nazanin, Perc Matjaž
School of Engineering , RMIT University , Melbourne, Victoria , Australia.
Department of Computer Engineering , Sharif University of Technology , Tehran , Iran.
R Soc Open Sci. 2017 Feb 8;4(2):160863. doi: 10.1098/rsos.160863. eCollection 2017 Feb.
Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
在线社交网络在现代社会中发挥着重要作用，并且它们塑造了社会关系演变的方式。社交网络中的链接预测有许多潜在应用，比如向用户推荐新项目、友情推荐以及发现虚假连接。许多真实的社交网络在多个层面（例如多个社交网络平台）上发展连接。在本文中，我们研究多层网络中的链接预测问题。作为一个例子，我们考虑一个由推特（作为一个微博服务）和四方网（作为一个基于位置的社交网络）组成的多层网络。我们考虑这两个平台中相同用户的社交网络，并开发一种基于元路径的算法来预测链接。两层的连接信息被用于预测四方网中的链接。三个经典分类器（朴素贝叶斯、支持向量机（SVM）和K近邻）被用于分类任务。尽管各层网络之间的相关性不高，但我们的实验表明，包含跨层信息能显著提高预测性能。支持向量机分类器的性能最佳，平均准确率为89%。
