Latent Space Models for Dynamic Networks with Weighted Edges

05/17/2020
by   Daniel K. Sewell, et al.
0

Longitudinal binary relational data can be better understood by implementing a latent space model for dynamic networks. This approach can be broadly extended to many types of weighted edges by using a link function to model the mean of the dyads, or by employing a similar strategy via data augmentation. To demonstrate this, we propose models for count dyads and for non-negative real dyads, analyzing simulated data and also both mobile phone data and world export/import data. The model parameters and latent actors' trajectories, estimated by Markov chain Monte Carlo algorithms, provide insight into the network dynamics.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/28/2021

Variational Inference for Latent Space Models for Dynamic Networks

Latent space models are popular for analyzing dynamic network data. We p...
research
05/18/2020

Latent Space Models for Dynamic Networks

Dynamic networks are used in a variety of fields to represent the struct...
research
11/23/2022

A Latent Shrinkage Position Model for Binary and Count Network Data

Interactions between actors are frequently represented using a network. ...
research
04/10/2022

Dynamic latent space relational event model

Dynamic relational processes, such as e-mail exchanges, bank loans and s...
research
07/10/2018

Node-specific effects in latent space modelling of multidimensional networks

Observed multidimensional network data can have different levels of comp...
research
12/19/2021

Sequential Estimation of Temporally Evolving Latent Space Network Models

In this article we focus on dynamic network data which describe interact...
research
08/23/2023

Semiparametric Modeling and Analysis for Longitudinal Network Data

We introduce a semiparametric latent space model for analyzing longitudi...

Please sign up or login with your details

Forgot password? Click here to reset