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Activity Number: 307 - Novel Approaches for Analyzing Dynamic Networks
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #303009
Title: Random Graph Hidden Markov Models for Percolation in Noisy Dynamic Networks
Author(s): Xiaojing Zhu* and Eric Kolaczyk and Heather Shappell
Companies: and Boston University and Johns Hopkins University
Keywords: percolation; random graph; hidden Markov model; noisy dynamic networks; particle filtering

In the study of random networks, percolation – the sudden emergence of a giant connected component (GCC) – is of fundamental interest. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of both edge birth and edge death. This class subsumes a variety of random graph models already used in studying noise-free percolation. We focus on parameter estimation and testing of putative percolation regimes.  We present an Expectation-Maximization (EM) algorithm, incorporating data augmentation and particle filtering, for estimating parameters in the model with a given sequence of noisy networks observed only at a longitudinal subsampling of time points. This in turn facilitates development of hypothesis testing strategies aimed ultimately at inferring putative percolation mechanisms in epileptic seizures.

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

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