{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hurdle-network-model-with-latent-dynamic","title":"Hurdle Network Model With Latent Dynamic Shrinkage For Enhanced Edge Prediction in Zero-Inflated Directed Network Time Series","arxiv_id":"2504.21275","date":"2025-04-30","proceeding":null,"authors":["Sandipan Pramanik","Raymond Robertson","Yang Ni"],"abstract":"This article aims to model international trade relationships among 29 countries in the apparel industry between 1994 and 2013. Bilateral trade flows can be represented as a directed network, where nodes correspond to countries and directed edges indicate trade flows (i.e., whether one country exported to another in a given year). Additionally, node (e.g., GDP) and edge-specific (e.g., labor provision) covariates are also available. The study focuses on two key challenges: (1) capturing multiple forms of temporal and network dependence, and dependence on covariates; and (2) accounting for potential trade volume as an important but partially observed edge-specific covariate, which is only available for country pairs that engaged in trade. To address these challenges, we introduce the dynamic hurdle network model (Hurdle-Net) for zero-inflated directed network time series that incorporates several novel features. First, it represents the time series as a paired binary and continuous time series and utilizes a hurdle model that effectively handles sparsity in edge occurrence. Second, the model captures evolving network dependencies using node-specific latent variables governed by a dynamic shrinkage process. Third, it leverages a shared latent structure across the binary and continuous components, reflecting the fact that both networks involve the same nodes. Finally, the model employs a generalized logistic link function to relate edge occurrence to edge weight, allowing for a parsimonious and coherent hierarchical Bayesian framework that jointly models both network components. Compared to static or independent models, Hurdle-Net provides improved model selection, estimation, and prediction performance for analyzing international trade patterns. Its effectiveness is demonstrated through simulation studies and an application to bilateral trade flow data.","url_abs":"https://arxiv.org/abs/2504.21275v1","url_pdf":"https://arxiv.org/pdf/2504.21275v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"hurdle-network-model-with-latent-dynamic","repo_url":"https://github.com/sandy-pramanik/hurdlenet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}