The {goldfish} package offers a collection of tools
designed for applying statistical models to dynamic network data. It
primarily focus on models for relational event data, namely, sequences
of interactions between actors or entities within a network, enriched by
fine-grained time-stamps information. Relational event data emerge in
various domains, such as automatically collected data about interactions
in communication and social media research, social science studies using
social sensors, and archival network studies that provide in-depth
details regarding the timing or sequence of relational actions between
nodes.
Currently, the package includes the following models:
For detailed documentation on each model, including usage examples, users are encouraged to consult the package’s vignettes and help files:
You can install {goldfish} directly from CRAN:
install.packages("goldfish")To install the development version from GitHub, use the remotes package:
remotes::install_github("stocnet/goldfish", build_vignettes = TRUE)remotes::install_github("stocnet/goldfish@develop", build_vignettes = TRUE)Or by downloading and install the latest binary releases for all major OSes – Windows, Mac, and Linux – can be found here.
In some cases, you may get an error that does not allow installation
of {goldfish} from source on Mac OSX versions, including
under R 4.0.0. The error may relate to compiling the parts of
{goldfish} that are written in C++, or whether OpenMP (for
parallelisation) can be found.
Many installation woes can be solved by directing R to use Homebrew installed gcc. An
updated setting up instructions thanks to @timonelmer are available here.
More details can be found here (Thank you @Knieps for identifying this.). Other links that may be helpful include:
Please share feedback on which of these work and we will update the installation guide accordingly.
Below is a quick-start guide to using the {goldfish}
package. The dataset used in this example is an abbreviated version of
the MIT Social Evolution data (?Social_Evolution).
The main data objects required for the analysis are the node set(s)
defineNodes() and network(s) defineNetwork().
The node set object contains labels and attributes of the actors in the
network. In contrast, a network object contains the information of past
relational events between actors. By default,
defineNetwork() constructs an empty matrix, its dimensions
defined by the length of the nodeset(s). Data frames containing event
data that modify these data objects can be linked to them using the
linkEvents() method.
library(goldfish)
data("Social_Evolution")
callNetwork <- defineNetwork(nodes = actors, directed = TRUE) |> # 1
linkEvents(changeEvent = calls, nodes = actors) # 2The events data frame, which indicates the time-varying attributes in the node set, contains the following columns:
time: The time when the attribute changes, either a
numeric or POSIXct value.node: The node for which the attribute changes, a
character value that matches the label
variable in the node set.replace: The new value of the attribute, a
numeric value.The events data frame that details the relational events between actors contains the following columns:
time: The time when the event occurred, either a
numeric or POSIXct value.sender: The actor initiating the event, a
character value that matches the label
variable in the node set.receiver: The actor receiving the event, a
character value that matches the label
variable in the node set.increment or replace: A
numeric value indicating either the increment that the
relational event represents or the new value.The final step in defining the data objects is to identify the dependent events. Here we would like to model as the dependent variable the calls between individuals. We specify the event data frame and the node set.
callsDependent <- defineDependentEvents(
events = calls, nodes = actors,
defaultNetwork = callNetwork
)We specify our model using the standard R formula format like:
goldfish_dependent ~ effects(process_state_element)
We can see which effects are currently available and how to specify them here:
vignette("goldfishEffects")Now to estimate this model, we use the ?estimate
function.
mod00Rate <- estimate(
callsDependent ~ indeg + outdeg,
model = "DyNAM", subModel = "rate"
)
summary(mod00Rate)
#>
#> Call:
#> estimate(x = callsDependent ~ indeg + outdeg, model = "DyNAM",
#> subModel = "rate")
#>
#>
#> Coefficients:
#> Estimate Std. Error z-value Pr(>|z|)
#> indeg 0.551445 0.066344 8.3119 < 2.2e-16 ***
#> outdeg 0.263784 0.028386 9.2927 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Converged with max abs. score of 2e-05
#> Log-Likelihood: -1750.9
#> AIC: 3505.8
#> AICc: 3505.9
#> BIC: 3514
#> model: "DyNAM" subModel: "rate"
mod00Choice <- estimate(
callsDependent ~ inertia + recip + trans,
model = "DyNAM", subModel = "choice"
)
summary(mod00Choice)
#>
#> Call:
#> estimate(x = callsDependent ~ inertia + recip + trans, model = "DyNAM",
#> subModel = "choice")
#>
#>
#> Coefficients:
#> Estimate Std. Error z-value Pr(>|z|)
#> inertia 5.19690 0.17397 29.8725 < 2.2e-16 ***
#> recip 1.39802 0.17300 8.0812 6.661e-16 ***
#> trans -0.23036 0.21554 -1.0687 0.2852
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Converged with max abs. score of 7e-05
#> Log-Likelihood: -696.72
#> AIC: 1399.4
#> AICc: 1399.5
#> BIC: 1411.7
#> model: "DyNAM" subModel: "choice"This project is a joint collaboration between the Social Networks Lab at ETH Zürich and the Graduate Institute Geneva, and incorporates and supports several sub-projects.
Butts, C. T. 2008. “A Relational Event Framework for Social Action.” Sociological Methodology 38 (1): 155–200.
Hoffman, Marion, Per Block, Timon Elmer, and Christoph Stadtfeld. 2020. “A Model for the Dynamics of Face-to-Face Interactions in Social Groups.” Network Science 8 (S1): S4–25. https://doi.org/10.1017/nws.2020.3.
Stadtfeld, C., and P. Block. 2017. “Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events.” Sociological Science 4 (14): 318–52. https://doi.org/10.15195/v4.a14.
Stadtfeld, C., J. Hollway, and P. Block. 2017. “Dynamic Network Actor Models: Investigating Coordination Ties Through Time.” Sociological Methodology 47 (1): 1–40. https://doi.org/10.1177/0081175017709295.