Nothing
An Introduction to Latent Order Logistic (LOLOG) Network Models
In lolog: Latent Order Logistic Graph Models
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
out.width="80%",
dpi=120
)
Introduction to LOLOG
Latent Order Logistic (LOLOG) models are a general framework for generative statistical modeling of graph datasets motivated by the principle of network growth. This class of models is fully general and terms modeling different important network features can be mixed and matched to provide a rich generative
description of complex networks.
Networks tend to evolve over time. This evolution can take the form of the addition or deletion of a tie from the network, or the addition or deletion of a vertex. To motivate our model we consider a growth process, where each edge variable is sequentially considered for edge creation, and edges are not deleted.
In particular the lolog package uses a vertex ordering process for the motivating data generating process of the graph. For some networks, it may be reasonable to posit that vertices entered the network in some order that may or may not be random, and upon entering the network, edge variables connecting them to vertices already in the network are considered in a completely random order. The probability that an ordering of edge variables ($s$) occurs is defined as $p(s)$. The probability of a tie, given the network grown up to a time-point is modeled as a logistic regression
$$
\textrm{logit}\big(p(y_{s_t}=1 | \eta, y^{t-1}, s_{ \leq t})\big) = \theta \cdot c(y_{s_t}=1 | y^{t-1}, s_{ \leq t})
$$
where $s_{\leq t}$ is the growth order of the network up to time $t$, $y^{t-1}$ is the state of the graph at time $t-1$. $c(y_{s_t} | y^{t-1}, s_{ \leq t})$ is a vector representing the change in some graph statistics from time $t-1$ to $t$ if an edge is present, and $\theta$ is a vector of parameters.
The full likelihood of an observed graph $y$ given an order $s$ is then just the product of these logistic likelihoods
$$
p(y|s,\theta) = \prod_{t=1}^{n_d}p(y_{s_t}=1 | \eta, y^{t-1}, s_{ \leq t}),
$$
and the marginal likelihood of an observed graph is the sum over all possible orderings
$$
p(y | \theta) = \sum_s p(y | s,\theta) p(s).
$$
Since the ordering of edge variable creation is rarely fully observed, the goal of this package is to perform graph inference on this marginal likelihood. This is done via Method of Moments, Generalized Method of Moments, or variational inference. The following document will show how this is operationalized within the lolog package.
Getting Started
This vignette is based on the vignette from the ergm package, and is designed to give a working introduction to LOLOG modeling.
This vignette will utilize the statnet suite of packages for data and network manipulation. To install these:
install.packages("statnet")
To install the latest version of the package from CRAN (not yet available):
install.packages("lolog")
To install the latest development version from github run the following:
# If devtools is not installed:
# install.packages("devtools")
devtools::install_github("statnet/lolog")
If this is your first R source package that you have installed, you’ll also need a set of development tools. On Windows, download and install Rtools, and devtools takes care of the rest. On a Mac, install the Xcode command line tools. On Linux, install the R development package, usually called r-devel or r-base-dev. For details see Package Development Prerequisites.
Statistical Network Modeling: The lolog Command and lolog Object
Make sure the lolog package is attached:
library(lolog)
set.seed(1)
The ergm package contains several network data sets that you can use
for practice examples.
suppressPackageStartupMessages(library(ergm))
#data(package='ergm') # tells us the datasets in our packages
data(florentine) # loads flomarriage and flobusiness data
flomarriage # Let's look at the flomarriage data
plot(flomarriage) # Let's view the flomarriage network
Networks tend to evolve over time. This evolution can take the form of the addition or deletion of a tie from the network, or the addition or deletion of a vertex. LOLOGs are motivated by a growth process, where each edge variable is sequentially considered for edge creation, and edges are not deleted.
Remember a LOLOG represents the probability of a tie, given the network grown up to a time-point as
$$
\textrm{logit}\big(p(y_{s_t}=1 | \eta, y^{t-1}, s_{ \leq t})\big) = \theta \cdot c(y_{s_t}=1 | y^{t-1}, s_{ \leq t})
$$
where $s_{\leq t}$ is the growth order of the network up to time $t$, $y^{t-1}$ is the state of the graph at time $t-1$. $c(y_{s_t} | y^{t-1}, s_{ \leq t})$ is a vector representing the change in graph statistics from time $t-1$ to $t$ if an edge is present, and $\theta$ is a vector of parameters.
An Erdos-Renyi Model
We begin with the simplest possible model, the Bernoulli or Erdos-Renyi model, which contains only an edge term.
flomodel.01 <- lolog(flomarriage~edges) # fit model
flomodel.01
summary(flomodel.01) # look in more depth
How to interpret this model? The log-odds of any tie occurring is:
$$
=-1.609\times\mbox{change in the number of ties} \=-1.609\times1
$$
for all ties, since the addition of any tie to the network changes
the number of ties by 1!
Corresponding probability is:
$$
\exp(-1.609)/(1+\exp(-1.609)) = 0.1667
$$
which is what you would expect, since there are 20/120 ties.
A Triangle Model
Let's add a term often thought to be a measure of clustering: the number of completed
triangles.
flomodel.02 <- lolog(flomarriage~edges()+triangles(), verbose=FALSE)
summary(flomodel.02)
coef1 = flomodel.02$theta[1]
coef2 = flomodel.02$theta[2]
logodds = coef1 + c(0,1,2) * coef2
expit = function(x) 1/(1+exp(-x))
ps = expit(logodds)
coef1 = round(coef1, 3)
coef2 = round(coef2, 3)
logodds = round(logodds, 3)
ps = round(ps, 3)
Again, how to interpret coefficients?
Conditional log-odds of two actors forming a tie is:
$$
r coef1 \times \mbox{change in the number of ties} + r coef2 \times \mbox{change in number of triangles}
$$
- if the tie will not add any triangles to the network, its log-odds is: $
r coef1$.
- if it will add one triangle to the network, its log-odds is: $
r coef1+r coef2=r logodds[2]$
- if it will add two triangles to the network, its log-odds is: $
r coef1+r coef2\times2=r logodds[3]$
- the corresponding probabilities are
r ps[1], r ps[2], and r ps[3].
Let's take a closer look at the lolog object itself:
class(flomodel.02) # this has the class lolog
names(flomodel.02) # let's look straight at the lolog obj.
flomodel.02$theta
flomodel.02$formula
wealth <- flomarriage %v% 'wealth' # the %v% extracts vertex
wealth # attributes from a network
plot(flomarriage, vertex.cex=wealth/25) # network plot with vertex size
# proportional to wealth
The Effect of Wealth
We can test whether edge probabilities are a function of wealth:
flomodel.03 <- lolog(flomarriage~edges+nodeCov('wealth'))
summary(flomodel.03)
Yes, there is a significant positive wealth effect on the probability
of a tie.
Does Large Wealth Inequality Matter?
We can test whether edge probabilities are a function of wealth:
wdiff<-outer(flomarriage %v% "wealth", flomarriage %v% "wealth",function(x,y){abs(x-y)>20})
table(wdiff)
flomodel.04 <- lolog(flomarriage~edges+nodeCov('wealth')+edgeCov(wdiff,"inequality"))
summary(flomodel.04)
The inequality in wealth does seem to increase the probability
of a tie.
Reciprocity
Let's try a model or two on:
Is there a statistically significant tendency for ties to be reciprocated ('mutuality')?
data(samplk)
ls() # directed data: Sampson's Monks
samplk3
plot(samplk3)
sampmodel.01 <- lolog(samplk3~edges+mutual, verbose=FALSE)
summary(sampmodel.01)
Modeling Larger Networks
Let's try a larger network. First we will fit a dyad independent model.
data(faux.mesa.high)
mesa <- faux.mesa.high
mesa
plot(mesa, vertex.col='Grade')
#legend('bottomleft',fill=7:12,legend=paste('Grade',7:12),cex=0.75)
mesa %v% "GradeCat" <- as.character(mesa %v% "Grade")
fauxmodel.01 <- lolog(mesa ~edges + nodeMatch('GradeCat') + nodeMatch('Race'))
summary(fauxmodel.01)
Now lets try adding in transitivity and 2-stars (a measure of degree spread)
# This may take a minute or two
fauxmodel.02 <- lolog(mesa ~edges + nodeMatch('GradeCat') + nodeMatch('Race') +
triangles + star(2), verbose=FALSE)
summary(fauxmodel.02)
We see strong evidence of additional transitivity, but little evidence that the degrees have higher spread than expected given the other terms. With LOLOG models, we avoid some of the degeneracy problems present in ERGM models. Let's try the same model in ergm
fauxmodel.01.ergm <- ergm(mesa ~edges + nodematch('GradeCat') + nodematch('Race') +
triangles + kstar(2))
The preceding will fail if run. Of course it is highly recommended that you take great care in selecting terms for use with ergm for precisely the reason that many can lead to model degeneracy. A much better choice here for the ergm model would have been to use gwesp and gwdegree in place of triangles and 2-stars.
Specifying Vertex Inclusion Order
LOLOG models are motivated by a growth process where edge variables are "added" to the network one at a time. The package implements a sequential vetex ordering process. Specifically, vertices are "added" to the network in random order, and then edge variables connecting the added vertex to each other vertex already added are included in random order.
If you either observe or partially observe the inclusion order of vertices, this information can be included by specifying it in the model formula after a '|' character. In this example we will consider the network relations of a group of partners at a law firm. We observe the senority of the partner, the type of practice and the office where they work. (cSenority and cPractice are just categorical versions of the numerically coded practice and senority variables). It is plausible to posit that partners with more senority entered the law firm and hense the network prior to less senior individuals.
library(network)
data(lazega)
seniority <- as.numeric(lazega %v% "seniority") # Lower values are more senior
fit <- lolog(lazega ~ edges() + triangles() + nodeCov("cSeniority") +
nodeCov("cPractice") + nodeMatch("gender") + nodeMatch("practice") +
nodeMatch("office") | seniority, verbose=FALSE)
summary(fit)
In some cases only a partial ordering is observed. For example we might observed the month someone joined a group, with multiple individuals joining per month. When ties like this exist in the ordering variable, network simulations respect the partial ordering, with ties broken at random for each simulated network.
Model Terms Available for LOLOG Estimation and Simulation
Model terms are the expressions (e.g., triangle) used to represent predictors on the right-hand size of equations used
in:
- calls to
lolog (to estimate an LOLOG model)
- calls to simulate (to simulate networks from an LOLOG model
fit)
- calls to calculateStatistics (to obtain measurements of network statistics
on a dataset)
Terms Provided with lolog
For a list of available terms that can be used to specify a LOLOG model,
type:
help('lolog-terms')
This currently produces:
edges (dyad-independent) (order-independent) (directed) (undirected) star(k, direction=1L) (order-independent) (directed) (undirected) triangles() (order-independent) (directed) (undirected) clustering() (order-independent) (undirected) transitivity() (order-independent) (undirected) mutual() (order-independent) (directed)nodeCov(name) (dyad-independent) (order-independent) (directed) (undirected) nodeMatch(name) (dyad-independent) (order-independent) (directed) (undirected) nodeMix(name) (dyad-independent) (order-independent) (directed) (undirected)edgeCov(x) (dyad-independent) (order-independent) (directed) (undirected) edgeCovSparse(x) (dyad-independent) (order-independent) (directed) (undirected) degree(d, direction=0L, lessThanOrEqual=FALSE) (order-independent) (directed) (undirected) degreeCrossProd() (order-independent) (undirected) gwdsp(alpha) (order-independent) (directed) (undirected) gwesp(alpha) (order-independent) (directed) (undirected) gwdegree(alpha, direction=0L) (order-independent) (directed) (undirected)esp(d) (order-independent) (directed) (undirected)geoDist(long, lat, distCuts=Inf) (dyad-independent) (order-independent) (undirected)dist(names (dyad-independent) (order-independent) (undirected)preferentialAttachment(k=1) (undirected)sharedNbrs(k=1) (undirected)nodeLogMaxCov(name) (order-independent) (undirected)twoPath() (order-independent) (directed) (undirected)boundedDegree(lower,upper) (order-independent) (undirected)
Coding New Terms
Full details on coding new terms is beyond the scope of this document. However, lolog provides facilities for extending the package to provide new terms. An example of this can be created via
lologPackageSkeleton()
and then at the command prompt, run
```{sh, eval=FALSE}
R CMD build LologExtension
R CMD INSTALL LologExtension_1.0.tar.gz
A new package will be installed implementing the minDegree (minimum degree) statistic.
Alternatively, C++ extensions can be added directly from R without the need of any package infrastructure via the inlining features of the Rcpp package. `lolog` provides an inline plugin for this feature. For details, see
```r
help("inlineLologPlugin")
Network Simulation and Computation
Network Statistics can be calculated using the calculateStatistics function
calculateStatistics(mesa ~ edges + triangles + degree(0:15))
Simulating from a fitted lolog object with
nets <- simulate(flomodel.03,nsim=10) #Generates a list of BinaryNet objects
plot(nets[[1]])
Voila. Of course, networks that you generate will look somewhat different. Note that the objects created are BinaryNet objects unless the convert parameter is set to TRUE, in which case they are network objects.
Working with BinaryNet Objects
BinaryNets are network data structures native to the lolog package. They are special in a couple ways. First, they have a sparse representation of missingness, such that a directed network where halve of the nodes have been egocentrically sampled takes the same amount of space as a simple fully observed network. Secondly, they may be passed up and down easily from R to C++ and vis a versa. Finally, they are extensible on the C++ level, so that their implementation may be replaced. For example, it might be useful to put in a file backed storage system for large problems.
BinaryNets can be coerced to and from network objects.
data(sampson)
#coersion
net <- as.BinaryNet(samplike)
nw2 <- as.network(net)
print(nw2)
#dyad Extraction
net[1:2,1:5]
net$outNeighbors(c(1,2,3))
#dyad assignment
net[1,1:5] <- rep(NA,5)
net[1:2,1:5]
net[1:2,1:5,maskMissing=FALSE] #remove the mask over missing values and see
#nothing was really changed
#node variables
net$variableNames()
net[["group"]]
net[["rnorm"]] <- rnorm(18)
net[["rnorm"]]
#See available methods
#print(DirectedNet)
#print(UndirectedNet)
All user facing functions in the lolog package accept BinaryNets as arguments, and will convert network, igraph and tidygraph graph objects to BinaryNets automatically.
Order Dependent Terms
LOLOG allows for network statistics that depend not just on the network, but also the (unobserved) order in which dyads were 'added' to the network. One model of this class is Barabasi-Albert preferential attachment model, which is closely approximated by a LOLOG model with an edges and preferentialAttachment term.
For each order dependent statistic, one or more order independent statistics must be specified as moment matching targets. In this case, we will use a
two-star term:
flomodel.04 <- lolog(flomarriage ~ edges() + preferentialAttachment(),
flomarriage ~ star(2), verbose=FALSE)
summary(flomodel.04)
Examining the Quality of Model Fit - GOF
LOLOGs are generative models - that is, they represent the process that governs tie formation at a local level. These local processes in turn aggregate up to produce characteristic global network
properties, even though these global properties are not explicit terms in the model. One test of whether a model "fits the data" is therefore how well it reproduces these global properties. We do this by choosing a network statistic that is not in the model, and comparing the value of this statistic observed in the original network to the distribution of values we get in simulated networks from our model.
We begin by comparing the degree structure of simulated networks compared to the observed (\textcolor{red}{red})
gdeg <- gofit(flomodel.03, flomarriage ~ degree(0:10))
gdeg
plot(gdeg)
Next we can look at the edgewise shared partner distribution in simulated networks compared with the observed distribution (\textcolor{red}{red})
gesp <- gofit(flomodel.03, flomarriage ~ esp(0:5))
gesp
plot(gesp)
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width="80%", dpi=120 )
Introduction to LOLOG
Latent Order Logistic (LOLOG) models are a general framework for generative statistical modeling of graph datasets motivated by the principle of network growth. This class of models is fully general and terms modeling different important network features can be mixed and matched to provide a rich generative description of complex networks.
Networks tend to evolve over time. This evolution can take the form of the addition or deletion of a tie from the network, or the addition or deletion of a vertex. To motivate our model we consider a growth process, where each edge variable is sequentially considered for edge creation, and edges are not deleted.
In particular the lolog package uses a vertex ordering process for the motivating data generating process of the graph. For some networks, it may be reasonable to posit that vertices entered the network in some order that may or may not be random, and upon entering the network, edge variables connecting them to vertices already in the network are considered in a completely random order. The probability that an ordering of edge variables ($s$) occurs is defined as $p(s)$. The probability of a tie, given the network grown up to a time-point is modeled as a logistic regression
$$
\textrm{logit}\big(p(y_{s_t}=1 | \eta, y^{t-1}, s_{ \leq t})\big) = \theta \cdot c(y_{s_t}=1 | y^{t-1}, s_{ \leq t})
$$
where $s_{\leq t}$ is the growth order of the network up to time $t$, $y^{t-1}$ is the state of the graph at time $t-1$. $c(y_{s_t} | y^{t-1}, s_{ \leq t})$ is a vector representing the change in some graph statistics from time $t-1$ to $t$ if an edge is present, and $\theta$ is a vector of parameters.
The full likelihood of an observed graph $y$ given an order $s$ is then just the product of these logistic likelihoods $$ p(y|s,\theta) = \prod_{t=1}^{n_d}p(y_{s_t}=1 | \eta, y^{t-1}, s_{ \leq t}), $$ and the marginal likelihood of an observed graph is the sum over all possible orderings $$ p(y | \theta) = \sum_s p(y | s,\theta) p(s). $$
Since the ordering of edge variable creation is rarely fully observed, the goal of this package is to perform graph inference on this marginal likelihood. This is done via Method of Moments, Generalized Method of Moments, or variational inference. The following document will show how this is operationalized within the lolog package.
Getting Started
This vignette is based on the vignette from the ergm package, and is designed to give a working introduction to LOLOG modeling.
This vignette will utilize the statnet suite of packages for data and network manipulation. To install these:
install.packages("statnet")
To install the latest version of the package from CRAN (not yet available):
install.packages("lolog")
To install the latest development version from github run the following:
# If devtools is not installed: # install.packages("devtools") devtools::install_github("statnet/lolog")
If this is your first R source package that you have installed, you’ll also need a set of development tools. On Windows, download and install Rtools, and devtools takes care of the rest. On a Mac, install the Xcode command line tools. On Linux, install the R development package, usually called r-devel or r-base-dev. For details see Package Development Prerequisites.
Statistical Network Modeling: The lolog Command and lolog Object
Make sure the lolog package is attached:
library(lolog) set.seed(1)
The ergm package contains several network data sets that you can use
for practice examples.
suppressPackageStartupMessages(library(ergm)) #data(package='ergm') # tells us the datasets in our packages data(florentine) # loads flomarriage and flobusiness data flomarriage # Let's look at the flomarriage data plot(flomarriage) # Let's view the flomarriage network
Networks tend to evolve over time. This evolution can take the form of the addition or deletion of a tie from the network, or the addition or deletion of a vertex. LOLOGs are motivated by a growth process, where each edge variable is sequentially considered for edge creation, and edges are not deleted.
Remember a LOLOG represents the probability of a tie, given the network grown up to a time-point as $$ \textrm{logit}\big(p(y_{s_t}=1 | \eta, y^{t-1}, s_{ \leq t})\big) = \theta \cdot c(y_{s_t}=1 | y^{t-1}, s_{ \leq t}) $$ where $s_{\leq t}$ is the growth order of the network up to time $t$, $y^{t-1}$ is the state of the graph at time $t-1$. $c(y_{s_t} | y^{t-1}, s_{ \leq t})$ is a vector representing the change in graph statistics from time $t-1$ to $t$ if an edge is present, and $\theta$ is a vector of parameters.
An Erdos-Renyi Model
We begin with the simplest possible model, the Bernoulli or Erdos-Renyi model, which contains only an edge term.
flomodel.01 <- lolog(flomarriage~edges) # fit model flomodel.01 summary(flomodel.01) # look in more depth
How to interpret this model? The log-odds of any tie occurring is: $$ =-1.609\times\mbox{change in the number of ties} \=-1.609\times1 $$ for all ties, since the addition of any tie to the network changes the number of ties by 1!
Corresponding probability is: $$ \exp(-1.609)/(1+\exp(-1.609)) = 0.1667 $$ which is what you would expect, since there are 20/120 ties.
A Triangle Model
Let's add a term often thought to be a measure of clustering: the number of completed triangles.
flomodel.02 <- lolog(flomarriage~edges()+triangles(), verbose=FALSE) summary(flomodel.02)
coef1 = flomodel.02$theta[1] coef2 = flomodel.02$theta[2] logodds = coef1 + c(0,1,2) * coef2 expit = function(x) 1/(1+exp(-x)) ps = expit(logodds) coef1 = round(coef1, 3) coef2 = round(coef2, 3) logodds = round(logodds, 3) ps = round(ps, 3)
Again, how to interpret coefficients?
Conditional log-odds of two actors forming a tie is:
$$
r coef1 \times \mbox{change in the number of ties} + r coef2 \times \mbox{change in number of triangles}
$$
- if the tie will not add any triangles to the network, its log-odds is: $
r coef1$. - if it will add one triangle to the network, its log-odds is: $
r coef1+r coef2=r logodds[2]$ - if it will add two triangles to the network, its log-odds is: $
r coef1+r coef2\times2=r logodds[3]$ - the corresponding probabilities are
r ps[1],r ps[2], andr ps[3].
Let's take a closer look at the lolog object itself:
class(flomodel.02) # this has the class lolog names(flomodel.02) # let's look straight at the lolog obj.
flomodel.02$theta flomodel.02$formula wealth <- flomarriage %v% 'wealth' # the %v% extracts vertex wealth # attributes from a network plot(flomarriage, vertex.cex=wealth/25) # network plot with vertex size # proportional to wealth
The Effect of Wealth
We can test whether edge probabilities are a function of wealth:
flomodel.03 <- lolog(flomarriage~edges+nodeCov('wealth')) summary(flomodel.03)
Yes, there is a significant positive wealth effect on the probability of a tie.
Does Large Wealth Inequality Matter?
We can test whether edge probabilities are a function of wealth:
wdiff<-outer(flomarriage %v% "wealth", flomarriage %v% "wealth",function(x,y){abs(x-y)>20}) table(wdiff) flomodel.04 <- lolog(flomarriage~edges+nodeCov('wealth')+edgeCov(wdiff,"inequality")) summary(flomodel.04)
The inequality in wealth does seem to increase the probability of a tie.
Reciprocity
Let's try a model or two on:
Is there a statistically significant tendency for ties to be reciprocated ('mutuality')?
data(samplk) ls() # directed data: Sampson's Monks samplk3 plot(samplk3) sampmodel.01 <- lolog(samplk3~edges+mutual, verbose=FALSE) summary(sampmodel.01)
Modeling Larger Networks
Let's try a larger network. First we will fit a dyad independent model.
data(faux.mesa.high) mesa <- faux.mesa.high mesa plot(mesa, vertex.col='Grade') #legend('bottomleft',fill=7:12,legend=paste('Grade',7:12),cex=0.75) mesa %v% "GradeCat" <- as.character(mesa %v% "Grade") fauxmodel.01 <- lolog(mesa ~edges + nodeMatch('GradeCat') + nodeMatch('Race')) summary(fauxmodel.01)
Now lets try adding in transitivity and 2-stars (a measure of degree spread)
# This may take a minute or two fauxmodel.02 <- lolog(mesa ~edges + nodeMatch('GradeCat') + nodeMatch('Race') + triangles + star(2), verbose=FALSE) summary(fauxmodel.02)
We see strong evidence of additional transitivity, but little evidence that the degrees have higher spread than expected given the other terms. With LOLOG models, we avoid some of the degeneracy problems present in ERGM models. Let's try the same model in ergm
fauxmodel.01.ergm <- ergm(mesa ~edges + nodematch('GradeCat') + nodematch('Race') + triangles + kstar(2))
The preceding will fail if run. Of course it is highly recommended that you take great care in selecting terms for use with ergm for precisely the reason that many can lead to model degeneracy. A much better choice here for the ergm model would have been to use gwesp and gwdegree in place of triangles and 2-stars.
Specifying Vertex Inclusion Order
LOLOG models are motivated by a growth process where edge variables are "added" to the network one at a time. The package implements a sequential vetex ordering process. Specifically, vertices are "added" to the network in random order, and then edge variables connecting the added vertex to each other vertex already added are included in random order.
If you either observe or partially observe the inclusion order of vertices, this information can be included by specifying it in the model formula after a '|' character. In this example we will consider the network relations of a group of partners at a law firm. We observe the senority of the partner, the type of practice and the office where they work. (cSenority and cPractice are just categorical versions of the numerically coded practice and senority variables). It is plausible to posit that partners with more senority entered the law firm and hense the network prior to less senior individuals.
library(network) data(lazega) seniority <- as.numeric(lazega %v% "seniority") # Lower values are more senior fit <- lolog(lazega ~ edges() + triangles() + nodeCov("cSeniority") + nodeCov("cPractice") + nodeMatch("gender") + nodeMatch("practice") + nodeMatch("office") | seniority, verbose=FALSE) summary(fit)
In some cases only a partial ordering is observed. For example we might observed the month someone joined a group, with multiple individuals joining per month. When ties like this exist in the ordering variable, network simulations respect the partial ordering, with ties broken at random for each simulated network.
Model Terms Available for LOLOG Estimation and Simulation
Model terms are the expressions (e.g., triangle) used to represent predictors on the right-hand size of equations used
in:
- calls to
lolog(to estimate an LOLOG model) - calls to simulate (to simulate networks from an LOLOG model fit)
- calls to calculateStatistics (to obtain measurements of network statistics on a dataset)
Terms Provided with lolog
For a list of available terms that can be used to specify a LOLOG model, type:
help('lolog-terms')
This currently produces:
edges(dyad-independent) (order-independent) (directed) (undirected)star(k, direction=1L)(order-independent) (directed) (undirected)triangles()(order-independent) (directed) (undirected)clustering()(order-independent) (undirected)transitivity()(order-independent) (undirected)mutual()(order-independent) (directed)nodeCov(name)(dyad-independent) (order-independent) (directed) (undirected)nodeMatch(name)(dyad-independent) (order-independent) (directed) (undirected)nodeMix(name)(dyad-independent) (order-independent) (directed) (undirected)edgeCov(x)(dyad-independent) (order-independent) (directed) (undirected)edgeCovSparse(x)(dyad-independent) (order-independent) (directed) (undirected)degree(d, direction=0L, lessThanOrEqual=FALSE)(order-independent) (directed) (undirected)degreeCrossProd()(order-independent) (undirected)gwdsp(alpha)(order-independent) (directed) (undirected)gwesp(alpha)(order-independent) (directed) (undirected)gwdegree(alpha, direction=0L)(order-independent) (directed) (undirected)esp(d)(order-independent) (directed) (undirected)geoDist(long, lat, distCuts=Inf)(dyad-independent) (order-independent) (undirected)dist(names(dyad-independent) (order-independent) (undirected)preferentialAttachment(k=1)(undirected)sharedNbrs(k=1)(undirected)nodeLogMaxCov(name)(order-independent) (undirected)twoPath()(order-independent) (directed) (undirected)boundedDegree(lower,upper)(order-independent) (undirected)
Coding New Terms
Full details on coding new terms is beyond the scope of this document. However, lolog provides facilities for extending the package to provide new terms. An example of this can be created via
lologPackageSkeleton()
and then at the command prompt, run ```{sh, eval=FALSE} R CMD build LologExtension R CMD INSTALL LologExtension_1.0.tar.gz
A new package will be installed implementing the minDegree (minimum degree) statistic.
Alternatively, C++ extensions can be added directly from R without the need of any package infrastructure via the inlining features of the Rcpp package. `lolog` provides an inline plugin for this feature. For details, see
```r
help("inlineLologPlugin")
Network Simulation and Computation
Network Statistics can be calculated using the calculateStatistics function
calculateStatistics(mesa ~ edges + triangles + degree(0:15))
Simulating from a fitted lolog object with
nets <- simulate(flomodel.03,nsim=10) #Generates a list of BinaryNet objects plot(nets[[1]])
Voila. Of course, networks that you generate will look somewhat different. Note that the objects created are BinaryNet objects unless the convert parameter is set to TRUE, in which case they are network objects.
Working with BinaryNet Objects
BinaryNets are network data structures native to the lolog package. They are special in a couple ways. First, they have a sparse representation of missingness, such that a directed network where halve of the nodes have been egocentrically sampled takes the same amount of space as a simple fully observed network. Secondly, they may be passed up and down easily from R to C++ and vis a versa. Finally, they are extensible on the C++ level, so that their implementation may be replaced. For example, it might be useful to put in a file backed storage system for large problems.
BinaryNets can be coerced to and from network objects.
data(sampson) #coersion net <- as.BinaryNet(samplike) nw2 <- as.network(net) print(nw2) #dyad Extraction net[1:2,1:5] net$outNeighbors(c(1,2,3)) #dyad assignment net[1,1:5] <- rep(NA,5) net[1:2,1:5] net[1:2,1:5,maskMissing=FALSE] #remove the mask over missing values and see #nothing was really changed #node variables net$variableNames() net[["group"]] net[["rnorm"]] <- rnorm(18) net[["rnorm"]] #See available methods #print(DirectedNet) #print(UndirectedNet)
All user facing functions in the lolog package accept BinaryNets as arguments, and will convert network, igraph and tidygraph graph objects to BinaryNets automatically.
Order Dependent Terms
LOLOG allows for network statistics that depend not just on the network, but also the (unobserved) order in which dyads were 'added' to the network. One model of this class is Barabasi-Albert preferential attachment model, which is closely approximated by a LOLOG model with an edges and preferentialAttachment term.
For each order dependent statistic, one or more order independent statistics must be specified as moment matching targets. In this case, we will use a two-star term:
flomodel.04 <- lolog(flomarriage ~ edges() + preferentialAttachment(), flomarriage ~ star(2), verbose=FALSE) summary(flomodel.04)
Examining the Quality of Model Fit - GOF
LOLOGs are generative models - that is, they represent the process that governs tie formation at a local level. These local processes in turn aggregate up to produce characteristic global network properties, even though these global properties are not explicit terms in the model. One test of whether a model "fits the data" is therefore how well it reproduces these global properties. We do this by choosing a network statistic that is not in the model, and comparing the value of this statistic observed in the original network to the distribution of values we get in simulated networks from our model.
We begin by comparing the degree structure of simulated networks compared to the observed (\textcolor{red}{red})
gdeg <- gofit(flomodel.03, flomarriage ~ degree(0:10)) gdeg plot(gdeg)
Next we can look at the edgewise shared partner distribution in simulated networks compared with the observed distribution (\textcolor{red}{red})
gesp <- gofit(flomodel.03, flomarriage ~ esp(0:5)) gesp plot(gesp)
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