Convergence guarantees for response prediction for latent structure network time series

作     者：Acharyya, Aranyak Passino, Francesco Sanna Trosset, Michael W. Priebe, Carey E. 

作者机构：The Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21218 United States The Department of Mathematics Imperial College London London United Kingdom The Department of Statistics Indiana University Bloomington BloomingtonIN47405 United States The Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2025年

核心收录：

主　　题：Network embeddings 

摘      要：In this article, we propose a technique to predict the response associated with an unlabeled time series of networks in a semisupervised setting. Our model involves a collection of time series of random networks of growing size, where some of the time series are associated with responses. Assuming that the collection of time series admits an unknown lower dimensional structure, our method exploits the underlying structure to consistently predict responses at the unlabeled time series of networks. Each time series represents a multilayer network on a common set of nodes, and raw stress embedding, a popular dimensionality reduction tool, is used for capturing the unknown latent low dimensional structure. Apart from establishing theoretical convergence guarantees and supporting them with numerical results, we demonstrate the use of our method in the analysis of real-world biological learning circuits of larval Drosophila. © 2025, CC BY.