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Activity Number: 495 - Statistical Methods for Networks
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #312699
Title: Dynamic Latent Space Network Models with Attractors for Flocking and Polarization
Author(s): Xiaojing Zhu* and Eric Kolaczyk and Konstantinos Spiliopoulos and Dylan Walker
Companies: Boston University and Boston University and Boston University and Boston University
Keywords: co-evolving networks ; latent space; attractor; polarization; flocking

In social systems, interactions frequently influence individual behavior and beliefs which can, in turn, impact interaction. From the perspective of complex networks, such phenomena have been usefully conceptualized as co-evolving networks, in which both the links between nodes and certain characteristics (or attributes) of nodes evolve over time, each in a way that impacts the other. To date, however, there has generally been far less attention given to statistical modeling and analysis for co-evolving networks. We develop a class of dynamic latent space network models with node attraction and edge persistence (DLSNM-ap) for characterizing social behaviors such as flocking, polarization and homophily in co-evolving networks. These models are potentially quite rich -- we initially focus on statistical inference and prediction for simplified variants of the model. We present an MCMC method for statistical inference and prediction tasks such as estimating parameters and inferring/predicting latent positions in the model with a given observed network time series.

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

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