Tensorial and bipartite block models for link prediction in layered networks and temporal networks
This work addresses the challenge of predicting interactions in complex multilayer systems, such as social and biological networks, but is incremental as it builds on existing stochastic block model frameworks.
The authors tackled the problem of link prediction in multilayer and temporal networks by introducing two stochastic block models, one node-based and one link-based, and developed scalable algorithms for inference. They applied these models to an email communication dataset and a drug interaction network, finding that simultaneous modeling of all layers generally improves accuracy, with the node-based model performing better for drug interactions and the link-based model for email communications.
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.