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Fast Simulation of Simple Temporal Exponential Random Graph Models
This package provides functions for the computationally efficient simulation and resimulation of dynamic networks estimated with the statistical framework of temporal exponential random graph models (TERGMs), implemented in the tergm package within the Statnet suite of R software. Networks are represented within an edgelist format only, with nodal attributes stored separately. Also includes efficient functions for the deletion and addition of nodes within that network representation.
The statistical framework of temporal exponential random graph models
(TERGMs) provides a rigorous, flexible approach to estimating generative
models for dynamic networks and simulating from them for the purposes of
modeling infectious disease transmission dynamics. TERGMs are used
within the EpiModel software package to do just that. While
estimation of these models is relatively fast, the resimulation of them
using the tools of the tergm package is computationally
burdensome, requiring hours to days to iteratively resimulate networks
with co-evolving demographic and epidemiological dynamics. The primary
reason for the computational burden is the use of the
network class of object (designed within the package of the
same name); these objects have tremendous flexibility in the types of
networks they represent but at the expense of object size. Continually
reading and writing larger-than-necessary data objects has the effect of
slowing the iterative dynamic simulations.
The tergmLite package reduces that computational burden
by representing networks less flexibly, but much more efficiently. For
epidemic models, the only types of networks that we typically estimate
and simulate from are undirected, binary edge networks with no missing
data (as it is simulated). Furthermore, the network history (edges or
node attributes) does not need to be stored for research-level
applications in which summary epidemiological statistics (e.g., disease
prevalence, incidence, and variations on those) at the population-level
are the standard output metrics for epidemic models. Therefore, the
network may be stored as a cross-sectional edgelist, which is a
two-column matrix of current edges between one node (in column one) and
another node (in column two). Attributes of the edges that are called
within ERGMs may be stored separately in vector format, as they are in
EpiModel. With this approach, the simulation time is sped
up by a factor of 25-50 fold, depending on the specific research
application.
Versions >= 2.0 implement the new networkLite API
that is implemented across the tergmLite,
network, and EpiModel packages. If you would
like to use the version before the implementation of this new API, you
should install version 1.2.0 with:
remotes::install_github("statnet/tergmLite@v1.2.0")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.