用于分层网络和时态网络中链路预测的张量和二分块模型
Tensorial and bipartite block models for link prediction in layered networks and temporal networks
摘要：
现实世界中的许多复杂系统都可以很好地表示为多层网络，预测这些系统中的相互作用是预测网络科学中最紧迫的问题之一。为了解决这一挑战，我们引入了两个随机块模型的多层和时间网络，其中一个使用节点作为其基本单元，而另一个侧重于链接。我们还开发了可扩展的算法来推断这些模型的参数。由于我们的模型同时描述了所有层，因此我们的方法在对任何特定层进行预测时都充分利用了整个网络中包含的信息。我们通过分析两个经验数据集来说明我们的方法的潜力-电子邮件通信的时间网络和治疗不同癌症类型的药物相互作用网络。我们发现，同时对所有层进行建模确实会导致更准确的链接预测。然而，最具预测性的模型取决于所考虑的数据集;而基于节点的模型更适合预测药物相互作用，基于链接的模型更适合预测电子邮件通信。
英文摘要：
Many real-world complex systems are well represented as multilayer networks; predicting interactions in those systems is one of the most pressing problems in predictive network science. To address this challenge, we introduce two stochastic block models for multilayer and temporal networks; one of them uses nodes as its fundamental unit, whereas the other focuses on links. We also develop scalable algorithms for inferring the parameters of these models. Because our models describe all layers simultaneously, our approach takes full advantage of the information contained in the whole network when making predictions about any particular layer. We illustrate the potential of our approach by analyzing two empirical datasets---a temporal network of email communications, and a network of drug interactions for treating different cancer types. We find that modeling all layers simultaneously does result, in general, in more accurate link prediction. However, the most predictive model depends on the dataset under consideration; whereas the node-based model is more appropriate for predicting drug interactions, the link-based model is more appropriate for predicting email communication.
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