Convergence guarantees for response prediction for latent structure network time series
File(s)
Accepted version
Author(s)
Acharyya, Aranyak
Sanna Passino, Francesco
Trosset, Michael W
Priebe, Carey E
Type
Journal Article
Abstract
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 and communication networks of interacting large language models.
Date Issued
2025-07-14
Date Acceptance
2025-07-03
Citation
IEEE Transactions on Network Science and Engineering, 2025, 13, pp.878-888
ISSN
2327-4697
Publisher
Institute of Electrical and Electronics Engineers
Start Page
878
End Page
888
Journal / Book Title
IEEE Transactions on Network Science and Engineering
Volume
13
Copyright Statement
Copyright © 2026, IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
http://arxiv.org/abs/2501.08456v1
Subjects
stat.ME
stat.ME
Publication Status
Published online
Date Publish Online
2025-07-14