A0684
Title: A dynamic stochastic block model for multidimensional networks
Authors: Ovielt Antonio Baltodano Lopez - Ca' Foscari University (Italy) [presenting]
Roberto Casarin - University Ca' Foscari of Venice (Italy)
Abstract: The availability of relational data can offer new insights into the functioning of the economy. Nevertheless, modeling the dynamics in network data with multiple types of relationships is still a challenging issue. Stochastic block models provide a parsimonious and flexible approach to network analysis. A new stochastic block model is proposed for multidimensional networks, where layer-specific hidden Markov-chain processes drive the changes in community formation. The changes in the block membership of a node in a given layer may be influenced by its own past membership in other layers. This allows for clustering overlap, clustering decoupling, or more complex relationships between layers, including settings of unidirectional or bidirectional, non-linear Granger block causality. The overparameterization issue of a saturated specification is coped with by assuming a Multi-Laplacian prior distribution within a Bayesian framework. Through simulations, it is shown that standard linear models and the pairwise approach are unable to detect block causality in most scenarios. In contrast, the model can recover the true Granger causality structure. As an application to international trade, it is shown that the model offers a unified framework, including community detection and the Gravity equation modeling. New evidence of block Granger causality of trade agreements and trade flows is found.
