A tutorial for modeling the evolution of network dynamics for multiple groups

Andrew Pilny; Luisa Ruge-Jones; Marshall Scott Poole

doi:10.3389/fhumd.2022.982066

A tutorial for modeling the evolution of network dynamics for multiple groups

Andrew Pilny
, Luisa Ruge-Jones
, Marshall Scott Poole

Research output: Contribution to journal › Article › peer-review

Abstract

Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested.

Original language	English
Article number	982066
Journal	Frontiers in Human Dynamics
Volume	4
DOIs	https://doi.org/10.3389/fhumd.2022.982066
State	Published - 2022

Bibliographical note

Publisher Copyright:
Copyright © 2023 Pilny, Ruge-Jones and Poole.

Funding

The data from this research was sponsored by the Army Research Laboratory (ARL) and was accomplished under Cooperative Agreements W911NF-09-2– 0053 and W5J9CQ-12-C-0017 (the ARL Network Science CTA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

Funders	Funder number
ARL Network Science CTA
DEVCOM Army Research Laboratory	W5J9CQ-12-C-0017, W911NF-09-2– 0053

Keywords

Bayesian estimation
evolution
multi-level model
networks
social network analysis
teams

ASJC Scopus subject areas

Demography
Sociology and Political Science
Human-Computer Interaction
Management, Monitoring, Policy and Law

Access to Document

10.3389/fhumd.2022.982066

Cite this

@article{c3822b31db5e47e19c1d99c102cfbc40,

title = "A tutorial for modeling the evolution of network dynamics for multiple groups",

abstract = "Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested.",

keywords = "Bayesian estimation, evolution, multi-level model, networks, social network analysis, teams",

author = "Andrew Pilny and Luisa Ruge-Jones and Poole, \{Marshall Scott\}",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 Pilny, Ruge-Jones and Poole.",

year = "2022",

doi = "10.3389/fhumd.2022.982066",

language = "English",

volume = "4",

}

TY - JOUR

T1 - A tutorial for modeling the evolution of network dynamics for multiple groups

AU - Pilny, Andrew

AU - Ruge-Jones, Luisa

AU - Poole, Marshall Scott

PY - 2022

Y1 - 2022

N2 - Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested.

AB - Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested.

KW - Bayesian estimation

KW - evolution

KW - multi-level model

KW - networks

KW - social network analysis

KW - teams

UR - https://www.scopus.com/pages/publications/85177808000

UR - https://www.scopus.com/pages/publications/85177808000#tab=citedBy

U2 - 10.3389/fhumd.2022.982066

DO - 10.3389/fhumd.2022.982066

M3 - Article

AN - SCOPUS:85177808000

VL - 4

JO - Frontiers in Human Dynamics

JF - Frontiers in Human Dynamics

M1 - 982066

ER -

A tutorial for modeling the evolution of network dynamics for multiple groups

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this