Most complex systems in the world are time-dependent and dynamic in nature, many of which are suitable to be modeled as dynamic networks that evolve over time. From the analysis of time-varying social networks to the analysis of functional brain networks in longitudinal study designs, new statistical methods are needed for a better understanding of network dynamics and the underlying complex systems. Our work revolves around statistical modeling, sampling and inference for dynamic networks driven by various applications. Specifically, we develop a class of random graph hidden Markov models (RGHMM) for percolation in noisy dynamic networks to infer the type of phase transitions undergone in epileptic seizures. We also develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models for characterizing coevolutionary phenomenon in social behaviors, such as flocking and polarization, and use it under the context of American politics to disentangle positive and negative partisanship in affective polarization. Finally, we provide uncertainty quantification in conjunction with estimation of the frequency of motifs in dynamic networks under a certain sampling model, by studying the asymptotics for streaming data applications.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44833 |
| Date | 05 July 2022 |
| Creators | Zhu, Xiaojing |
| Contributors | Kolaczyk, Eric D. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
Page generated in 0.0018 seconds