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Activity Number: 267 - Nonparametric Statistics Student Paper Competition Presentations
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #322638
Title: Continuous-Time Latent Process Network Models
Author(s): Peter MacDonald* and Elizaveta Levina and Ji Zhu
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: Latent space model; Multilayer; Multiplex; Dynamic network; B-spline
Abstract:

Network data are often collected through the observation of a complex system over time, leading to time-stamped network snapshots. Methods in statistical network analysis are traditionally designed for a single network, but applying these methods to a time-aggregated network can miss important temporal structure in the data. In this work, we provide an approach to estimating the expected network in continuous time. We parameterize the network expectation through time-varying positions, such that the activity of each node is governed by a low-dimensional latent process. To tractably estimate these processes, we assume their components come from a fixed, finite-dimensional function basis. We provide a gradient descent estimation approach, establish theoretical results for its convergence, compare our method to competitors, and apply it to a real dynamic network of international political interactions.


Authors who are presenting talks have a * after their name.

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