Link Prediction in Signed Social Networks: From Status Theory to Motif Families

Abstract

Link prediction can discover missing information and evolution mechanism of complex networks, so a huge number of novel algorithms have been proposed recently. However, the existing link prediction algorithms for directed signed networks only depend on motifs that satisfy status theory, and other types of motifs are rarely taken into account. In this study, first we propose a link prediction method based on the number of edge-dependent motifs, and explain it by a naive Bayes model. Furthermore, we put forward a Signed Local Naive Bayes (SLNB) model based on two kinds of different motifs, which has higher prediction performance than only considering a single motif. Finally, we combine all the 3-node motifs to form a motif family, and use a machine learning framework for link prediction. The results show that motif families can greatly improve the performance of link prediction. Moreover, according to the correlation between these predictors, the intrinsic relationship between different motifs can be discovered, and the computational complexity of link prediction can be reduced after feature selection. Our research can not only improve the performance of link prediction, but also be helpful to uncover the evolutionary mechanism of signed social networks.