Tensorial and bipartite block models for link prediction in layered networks and temporal networks

Abstract

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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• Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel
• The Atlas for the Aspiring Network Scientist
• The latent cognitive structures of social networks
• Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks
• Node metadata can produce predictability transitions in network inference problems
• Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling