Modeling Tie Duration in ERGM-Based Dynamic Network Models

03/14/2022
by   Pavel N. Krivitsky, et al.
0

Krivitsky and Handcock (2014) proposed a Separable Temporal ERGM (STERGM) framework for modeling social networks, which facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. In this note, we explore the hazard structures achievable in this framework, with first- and higher-order Markov assumptions, and propose ways to model a variety of duration distributions in this framework.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/14/2022

Modeling of Dynamic Networks based on Egocentric Data with Durational Information

Modeling of dynamic networks – networks that evolve over time – has mani...
research
11/03/2021

Autoregressive conditional duration modelling of high frequency data

This paper explores the duration dynamics modelling under the Autoregres...
research
02/29/2012

Inference in Hidden Markov Models with Explicit State Duration Distributions

In this letter we borrow from the inference techniques developed for unb...
research
10/19/2020

End-to-End Text-to-Speech using Latent Duration based on VQ-VAE

Explicit duration modeling is a key to achieving robust and efficient al...
research
05/26/2022

A Partially Separable Temporal Model for Dynamic Valued Networks

The Exponential-family Random Graph Model (ERGM) is a powerful statistic...
research
01/02/2018

Cautionary note on "Semiparametric modeling of grouped current duration data with preferential reporting'"

This report is designed to clarify a few points about the article "Semip...
research
07/03/2023

Mitigating the choice of the duration in DDMS models through a parametric link

One of the most important hyper-parameters in duration-dependent Markov-...

Please sign up or login with your details

Forgot password? Click here to reset