An Eigenmodel for Dynamic Multilayer Networks

Abstract

Dynamic multilayer networks frequently represent the structure of multiple co-evolving relations; however, statistical models are not well-developed for this prevalent network type. Here, we propose a new latent space model for dynamic multilayer networks. The key feature of our model is its ability to identify common time-varying structures shared by all layers while also accounting for layer-wise variation and degree heterogeneity. We establish the identifiability of the model's parameters and develop a structured mean-field variational inference approach to estimate the model's posterior, which scales to networks previously intractable to dynamic latent space models. We demonstrate the estimation procedure's accuracy and scalability on simulated networks. We apply the model to two real-world problems: discerning regional conflicts in a data set of international relations and quantifying infectious disease spread throughout a school based on the student's daily contact patterns.

arXiv comment55 pages, 18 figures

References in corpus (5)

• Dynamics of person-to-person interactions from distributed RFID sensor networks
• High resolution dynamical mapping of social interactions with active RFID
• Modeling homophily and stochastic equivalence in symmetric relational data
• Nonparametric Bayes dynamic modeling of relational data
• Latent Space Modeling of Multidimensional Networks with Application to the Exchange of Votes in Eurovision Song Contest