LOLOG is a general framework for generative statistical modeling of graph datasets motivated by the principle of network growth. This class of models is fully general and terms modeling different important network features can be mixed and matched to provide a rich generative description of complex networks.
- The mathematical details are outlined in a technical paper.
- For a more detailed description of what can be done with the
lolog
package, see the introductory vignette. - An application of LOLOG modeling to a UK Faculty data set with comparisons to an ERGM fit can be found here.
To install the latest development version from the github repo run:
devtools::install_github("statnet/lolog")
If you don't have a personal access token, you can create one in your profile page.
Alternatively you can manually clone the repo and install. First, make sure you have the dependencies installed:
install.packages(c("network","ggplot2","reshape2","Rcpp"))
For good measure, the suggested packages too
install.packages(c("testthat","inline","knitr","rmarkdown","ergm"))
Then from the command line run:
git clone https://github.com/statnet/lolog.git
R CMD build lolog
R CMD INSTALL lolog_*.tar.gz
library(lolog)
data(ukFaculty)
# Delete 2 vertices missing group
delete.vertices(ukFaculty, which(is.na(ukFaculty %v% "Group")))
# A dyad independent model
fitind <- lolog(ukFaculty ~ edges() + nodeMatch("GroupC") + nodeCov("GroupC"))
summary(fitind)
Development Practices and Policies for Contributers
This package is set up as an Eclipse project, and the C++ code can be compiled and run without reinstalling the package. To set up in your eclipse IDE, select import project -> General -> Existing Projects into Workspace and select the lolog directory.
This project was set up following the methods outlined in: