Hierarchical models for relational event sequences |
| |
Authors: | Christopher DuBois Carter T. Butts Daniel McFarland Padhraic Smyth |
| |
Affiliation: | 1. Department of Statistics, University of California, Irvine, United States;2. Department of Sociology, University of California, Irvine, United States;3. Institute for Mathematical Behavioral Sciences, University of California, Irvine, United States;4. Department of Computer Science, University of California, Irvine, United States;5. Department of Education, Stanford University, United States |
| |
Abstract: | Interaction within small groups can often be represented as a sequence of events, each event involving a sender and a recipient. Recent methods for modeling network data in continuous time model the rate at which individuals interact conditioned on the previous history of events as well as actor covariates. We present a hierarchical extension for modeling multiple such sequences, facilitating inferences about event-level dynamics and their variation across sequences. The hierarchical approach allows one to share information across sequences in a principled manner—we illustrate the efficacy of such sharing through a set of prediction experiments. After discussing methods for adequacy checking and model selection for this class of models, the method is illustrated with an analysis of high school classroom dynamics from 297 sessions. |
| |
Keywords: | Hierarchical Bayesian models Event-based network data Group dynamics |
本文献已被 ScienceDirect 等数据库收录! |
|