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.

中文翻译：

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签名社交网络中的链接预测：从地位理论到母题家族

链路预测可以发现复杂网络的缺失信息和进化机制，因此最近提出了大量新颖的算法。然而，现有的有向签名网络链接预测算法仅依赖于满足状态理论的模体，很少考虑其他类型的模体。在这项研究中，我们首先提出了一种基于边缘相关模体数量的链接预测方法，并通过朴素贝叶斯模型对其进行解释。此外，我们提出了一种基于两种不同模体的有符号局部朴素贝叶斯（SLNB）模型，该模型比仅考虑单个模体具有更高的预测性能。最后，我们将所有 3 节点motifs 组合起来形成一个motif family，并使用机器学习框架进行链接预测。结果表明，motif family 可以大大提高链接预测的性能。而且，根据这些预测器之间的相关性，可以发现不同模体之间的内在关系，在特征选择后可以降低链接预测的计算复杂度。我们的研究不仅可以提高链接预测的性能，而且有助于揭示签名社交网络的进化机制。