p* Models & Network Dynamics

Networks in the Global World 2014

9:30-11:30, 12:00-14:00 June 27, 2014

Room 67, 3rd Floor

http://goo.gl/l5XTkp

Benjamin Lind, PhD

Faculty of Sociology, Center for Advanced Studies

International Laboratory for Applied Network Research

Higher School of Economics, Moscow

What are Exponential Random Graph (p)* Models?

"Why Model Social Networks?"

Robins et al. 2007. Social Networks.

To understand regularities in tie formation
Can network structures emerge by chance?
Test competing explanations for the same outcome
Efficiently represent complex network data
Address relationship between local and global properties

Modeling Steps

Robins et al. 2007. Social Networks.

Each edge is a random variable
Propose dependence hypothesis
Only certain forms will meet dependence hypothesis

If there are none, the model is "degenerate"
If there are only a few, it is "near degenerate"

Simplify

Reduce parameters
Homogeneity assumption

Estimate and interpret parameters

Formula

Probability that a random graph is isomorphic to the observed

Pr(Y = y) = (1 / k) * exp{ΣA ηA*gA(Y)}

Y = A random graph
y = Observed graph
A = A network statistic
g(Y) = Vector of model statistics
η = Parameter vector
k = Normalizing constant

Dependence Assumptions

(Robins et al. 2007. Social Networks.)

Bernoulli graphs
Dyadic dependence
Markov random graphs

Which Network Statistics to Model?

Edges
Nodal attributes

Popularity effects
Homophily effects

Reciprocity
Triadic terms

The number of triangles
Shared partners

Dyadic or edge

Transitivity

Path terms
Cycles
...

Software

Be sure to use the most recent version!

We'll be using v. 3.1.0.

Software

statnet

Suite

network
ergm
sna
tergm
...

Uses "network" data objects
Written in both R and C
Not compatible with other R network packages

install.packages("statnet")
library(statnet)

Example Data

Grey's Anatomy "Hook-Up" Network

Popular night time medical drama
Actors: fictional hospital staff in Seattle, WA
Edges: sexual relationships
Actor attributes:

Sex, race
Birth year, astrological sign
Position
Season

Provided courtesy of Weissman and Lind

CC-BY-SA

Read the Data

Enter the following URLs into your browser in this order

Sociomatrix: http://goo.gl/Qcqd3u

Should be titled "Grey's Anatomy.tsv"

Attributes: http://goo.gl/vVLhrR

Should be titled "Grey's Anatomy (1).tsv"

(Download should begin automatically)

setwd("...") # Enter the folder where the above files were downloaded
ga.mat <- read.table("Grey's Anatomy.tsv", sep= "\t", header = TRUE, row.names = 1, quote = "\"")
ga.mat <- as.matrix(ga.mat)
ga.atts <- read.table("Grey's Anatomy (1).tsv", sep="\t", header=T, quote="\"", stringsAsFactors = FALSE, strip.white = TRUE, as.is = TRUE)
ga.net <- network(ga.mat, vertex.attr = ga.atts, vertex.attrnames = colnames(ga.atts), directed=F, hyper=F, loops=F, multiple=F, bipartite=F)

Plot the Data

Verify everything is properly read

Roughly understand the network

vcol = c("blue", "pink")[1 + (get.vertex.attribute(ga.net, "sex") == "F")]
plot(ga.net, vertex.col = vcol, label = get.vertex.attribute(ga.net, "name"), label.cex = 0.75)

Hypotheses?

Documentation

# Conduct a fuzzy search through the documentation for the term "ergm"
??"ergm"

# Read the documentation for the function 'ergm()'
?ergm

# Read the documentation for ergm terms in the ergm package
?ergm::ergm.terms

Let's take a few minutes to review the documentation

Our Baseline Model

Two Most Salient Network Characteristics

Number of edges (i.e., density effect)
Actor's sex (i.e., heterophily)

ga.base <- ergm(ga.net ~ edges + nodematch("sex")) # Estimate the model
summary(ga.base) # Summarize the model
==========================
Summary of model fit
==========================
Formula:   ga.net ~ edges + nodematch("sex")

Iterations:  20 

Monte Carlo MLE Results:
              Estimate Std. Error MCMC % p-value    
edges          -2.3003     0.1581     NA  <1e-04 ***
nodematch.sex  -3.1399     0.7260     NA  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  320.5  on 944  degrees of freedom
 
AIC: 324.5    BIC: 334.2    (Smaller is better.)

Interpretation

 ==========================
Summary of model fit
==========================

Formula:   ga.net ~ edges + nodematch("sex")

Iterations:  20 

Monte Carlo MLE Results:
              Estimate Std. Error MCMC % p-value    
edges          -2.3003     0.1581     NA  <1e-04 ***
nodematch.sex  -3.1399     0.7260     NA  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  320.5  on 944  degrees of freedom
 
AIC: 324.5    BIC: 334.2    (Smaller is better.)

Coefficients are conditional log-odds

Log-odds of a tie forming between two actors
I.e., edges + nodematch

Probability

exp(edges + nodematch) / (1 + exp(edges + nodematch))

Interpretation

==========================
Summary of model fit
==========================

Formula:   ga.net ~ edges + nodematch("sex")

Iterations:  20 

Monte Carlo MLE Results:
              Estimate Std. Error MCMC % p-value    
edges          -2.3003     0.1581     NA  <1e-04 ***
nodematch.sex  -3.1399     0.7260     NA  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  320.5  on 944  degrees of freedom
 
AIC: 324.5    BIC: 334.2    (Smaller is better.)

Probability

exp(edges + nodematch) / (1 + exp(edges + nodematch))
Opposite-sex = exp(-2.3 + -3.1 * 0) / (1 + exp(-2.3 + -3.1 * 0))
Same-sex = exp(-2.3 + -3.1 * 1) / (1 + exp(-2.3 + -3.1 * 1))
Correspond to 0.09 and 0.004

Interpretation

==========================
Summary of model fit
==========================

Formula:   ga.net ~ edges + nodematch("sex")

Iterations:  20 

Monte Carlo MLE Results:
              Estimate Std. Error MCMC % p-value    
edges          -2.3003     0.1581     NA  <1e-04 ***
nodematch.sex  -3.1399     0.7260     NA  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  320.5  on 944  degrees of freedom
 
AIC: 324.5    BIC: 334.2    (Smaller is better.)

Standard errors can be used for estimate confidence
p-values for statistical significance
Use either AIC or BIC to gauge model parsimony

Principle of Occam's Razor
Better fit with fewer parameters is preferable

Plot a Simulated Model

Does it look anything like our original network?

Is it degenerate?

plot(simulate(ga.base), vertex.col=vcol)

How is it different?

Is it degenerate?

Goodness of Fit

Simulate the network many times
Assess with one or more unmodeled network statistic
Compare the observed network statistics to the simulated

ga.base.gof <- gof(ga.base) # Simulate and measure. May take a minute or so.
summary(ga.base.gof) # Print a summary
par(mfrow=c(3, 1)) # Set up the plotting window
plot(ga.base.gof) # Plot the summary

Additional Parameter

Keep edges and nodematch(sex)

Theoretically motivated and significant

Account for vertices with a degree of 1

Theoretically meaningful and underrepresented

ga.base.d1 <- ergm(ga.net ~ edges + nodematch("sex") + degree(1))
summary(ga.base.d1)
==========================
Summary of model fit
==========================
Formula:   ga.net ~ edges + nodematch("sex") + degree(1)

Iterations:  20 

Monte Carlo MLE Results:
              Estimate Std. Error MCMC % p-value    
edges          -1.4292     0.2466      0  <1e-04 ***
nodematch.sex  -3.1766     0.7332      0  <1e-04 ***
degree1         2.0807     0.4602      0  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  295.8  on 943  degrees of freedom 
AIC: 301.8    BIC: 316.3    (Smaller is better.)

Additional Parameter

ga.base.d1 <- ergm(ga.net ~ edges + nodematch("sex") + degree(1))
summary(ga.base.d1)
==========================
Summary of model fit
==========================
Formula:   ga.net ~ edges + nodematch("sex") + degree(1)

Iterations:  20 

Monte Carlo MLE Results:
              Estimate Std. Error MCMC % p-value    
edges          -1.4292     0.2466      0  <1e-04 ***
nodematch.sex  -3.1766     0.7332      0  <1e-04 ***
degree1         2.0807     0.4602      0  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  295.8  on 943  degrees of freedom 
AIC: 301.8    BIC: 316.3    (Smaller is better.)

Iteration 1 of at most 20:
Convergence test P-value: 0e+00
The log-likelihood improved by 0.6971
Iteration 2 of at most 20: ...

Additional Parameter

ga.base.d1 <- ergm(ga.net ~ edges + nodematch("sex") + degree(1))
summary(ga.base.d1)
==========================
Summary of model fit
==========================
Formula:   ga.net ~ edges + nodematch("sex") + degree(1)

Iterations:  20 

Monte Carlo MLE Results:
              Estimate Std. Error MCMC % p-value    
edges          -1.4292     0.2466      0  <1e-04 ***
nodematch.sex  -3.1766     0.7332      0  <1e-04 ***
degree1         2.0807     0.4602      0  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  295.8  on 943  degrees of freedom 
AIC: 301.8    BIC: 316.3    (Smaller is better.)

Interpretation

Previous effects still significant
Monogamy effect is positive and significant
Improved AIC and BIC

Assess Fit

plot(simulate(ga.base.d1), vertex.col = vcol)

Assess Fit

plot(simulate(ga.base.d1), vertex.col = vcol)

ga.base.d1.gof <- gof(ga.base.d1)
summary(ga.base.d1.gof)
par(mfrow=c(3,1))
plot(ga.base.d1.gof)

Markov Chain Monte Carlo

Did it work properly?

mcmc.diagnostics(ga.base.d1)

# ...
Are sample statistics significantly different from observed?
               edges nodematch.sex    degree1 Overall (Chi^2)
diff.      0.3728000     0.0201000 -0.1174000              NA
test stat. 1.0569427     0.7868791 -0.7314219        1.563627
P-val.     0.2905377     0.4313526  0.4645215        0.667665

That looks okay, moving on...

Markov Chain Monte Carlo

Did it work properly?

Chain 1 
            edges nodematch.sex   degree1
Lag 0   1.0000000    1.00000000 1.0000000
Lag 100 0.8609309    0.46789330 0.7446234
Lag 200 0.7542179    0.25234066 0.6214649
Lag 300 0.6619003    0.15270586 0.5286271
Lag 400 0.5806592    0.08059618 0.4508049
Lag 500 0.5136180    0.04619048 0.3917378

Sample statistics burn-in diagnostic (Geweke):
Chain 1 

Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5 
        edges nodematch.sex       degree1 
      -0.6463       -1.4147        0.8218 
P-values (lower = worse):
        edges nodematch.sex       degree1 
    0.5180537     0.1571526     0.4111837

High autocorrelation across the chains
Geweke roughly corresponds to Z-statistic

Nodematch's p-value is a bit low.

Markov Chain Monte Carlo

Troubleshooting

Sample statistics differ from observed

MCMC's default sample size is 10,000
Increase it (maybe to ~20,000 or 50,000)

(Unnecessary here)

Autocorrelation

MCMC.interval's default is 100
Increase it (here, to ~5,000)

Improve the Geweke statistics

MCMC.burnin's default is 10,000
Increase it (here, to ~50,000)

Though they improve the results and help models converge, these changes increase estimation time.

Markov Chain Monte Carlo

Documentation to fine tune

?control.ergm

In addition to adjusting

MCMC.burnin
MCMC.samplesize
MCMC.interval

Consider setting "parallel = 4"

... or however many processors you'd like
Increases speed
Requires library(rlecuyer)

Markov Chain Monte Carlo

?control.ergm

# If we wanted to improve the sample statistics
# ga.base.d1 <- ergm(ga.net ~ edges + nodematch("sex") + degree(1), control = control.ergm(MCMC.burnin = 50000, MCMC.interval = 5000, MCMC.samplesize = 50000))

ga.base.d1 <- ergm(ga.net ~ edges + nodematch("sex") + degree(1), control = control.ergm(MCMC.burnin = 50000, MCMC.interval = 5000))
summary(ga.base.d1)
==========================
Summary of model fit
==========================
Formula:   ga.net ~ edges + nodematch("sex") + degree(1)
Iterations:  20 
Monte Carlo MLE Results:
              Estimate Std. Error MCMC % p-value    
edges          -1.4298     0.2483      0  <1e-04 ***
nodematch.sex  -3.1697     0.7257      0  <1e-04 ***
degree1         2.0794     0.4587      0  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  295.7  on 943  degrees of freedom

Markov Chain Monte Carlo

mcmc.diagnostics(ga.base.d1)
# ...
Are sample statistics significantly different from observed?
                 edges nodematch.sex degree1 Overall (Chi^2)
diff.      -0.00150000     0.0174000       0              NA
test stat. -0.01690472     1.1955138       0       1.6043633
P-val.      0.98651262     0.2318864       1       0.6584009

Sample statistics auto-correlation:
Chain 1 
                 edges nodematch.sex      degree1
Lag 0      1.000000000   1.000000000  1.000000000
Lag 5000   0.015469475   0.005331195  0.006627886
Lag 10000 -0.000798288  -0.004256179 -0.004036665
Lag 15000 -0.012238958  -0.002511807 -0.008747576
Lag 20000 -0.001466805   0.004430013  0.001075269
Lag 25000 -0.026182557  -0.010818715 -0.027159351

Sample statistics burn-in diagnostic (Geweke):
# ...

        edges nodematch.sex       degree1 
       1.0343        0.5097       -1.0479 

P-values (lower = worse):
        edges nodematch.sex       degree1 
    0.3010182     0.6102707     0.2946793

Alternative Theories

We now have a decent baseline model

Approximates degree distribution
Approximates geodesic distribution

Which theories should we now test?

Homophily

Attributes: age, race, position
Forms

Continuous vs. categorical
General, differential, mixture

Attribute-based popularity

Could challenge homophily effects
Attributes: age, race, position, sex

Age Homophily

Continuous measure, use 'absdiff()' term

ga.base.d1.age <- ergm(ga.net ~ edges + nodematch("sex") + degree(1) + absdiff("birthyear"), control=control.ergm(MCMC.burnin=50000, MCMC.interval=5000))
summary(ga.base.d1.age)

==========================
Summary of model fit
==========================
Formula:   ga.net ~ edges + nodematch("sex") + degree(1) + absdiff("birthyear")

Iterations:  20 

Monte Carlo MLE Results:
                  Estimate Std. Error MCMC % p-value    
edges             -0.46543    0.28588      0   0.104    
nodematch.sex     -3.23784    0.73100      0  <1e-04 ***
degree1            1.99880    0.44231      0  <1e-04 ***
absdiff.birthyear -0.12465    0.02929      0  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  270.3  on 942  degrees of freedom
 
AIC: 278.3    BIC: 297.7    (Smaller is better.)

Age Homophily

==========================
Summary of model fit
==========================
Formula:   ga.net ~ edges + nodematch("sex") + degree(1) + absdiff("birthyear")

Iterations:  20 

Monte Carlo MLE Results:
                  Estimate Std. Error MCMC % p-value    
edges             -0.46543    0.28588      0   0.104    
nodematch.sex     -3.23784    0.73100      0  <1e-04 ***
degree1            1.99880    0.44231      0  <1e-04 ***
absdiff.birthyear -0.12465    0.02929      0  <1e-04 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  270.3  on 942  degrees of freedom
 
AIC: 278.3    BIC: 297.7    (Smaller is better.)

Interpretation

Effects comparable to before
Age-based homophily present
Improved AIC and BIC

Age Homophily

Whenever using the MCMC, check its diagnostics

mcmc.diagnostics(ga.base.d1.age)

Does everything look okay?

If not...

Which diagnostic looks wrong?
How would we adjust the MCMC to correct it?

For sake of time, we'll pretend it's all okay.

Challenge to Age Homophily Hypothesis

What if younger characters/actors form more ties?

Reasons

Physiological
Audience interest
Cultural attitudes

Implications if true?

High probability of within the youth (homophily)

If most of the cast is young, homophily more apparent

Low probability of ties within the elderly
Medium probability of ties between youth and elderly

Challenge to Age Homophily Hypothesis

ga.base.d1.age.covage <- ergm(ga.net ~ edges + nodematch("sex") + degree(1) + absdiff("birthyear") + nodecov("birthyear"), control=control.ergm(MCMC.burnin=50000, MCMC.interval=5000))
summary(ga.base.d1.age.covage)

==========================
Summary of model fit
==========================

Monte Carlo MLE Results:
                   Estimate Std. Error MCMC % p-value    
edges             21.068917  50.797099      0   0.678    
nodematch.sex     -3.239118   0.740633      0  <1e-04 ***
degree1            1.984977   0.443934      0  <1e-04 ***
absdiff.birthyear -0.125028   0.029186      0  <1e-04 ***
nodecov.birthyear -0.005462   0.012884      0   0.672    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  270.1  on 941  degrees of freedom
 
AIC: 280.1    BIC: 304.4    (Smaller is better.)

Challenge to Age Homophily Hypothesis

==========================
Summary of model fit
==========================

Monte Carlo MLE Results:
                   Estimate Std. Error MCMC % p-value    
edges             21.068917  50.797099      0   0.678    
nodematch.sex     -3.239118   0.740633      0  <1e-04 ***
degree1            1.984977   0.443934      0  <1e-04 ***
absdiff.birthyear -0.125028   0.029186      0  <1e-04 ***
nodecov.birthyear -0.005462   0.012884      0   0.672    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  270.1  on 941  degrees of freedom
 
AIC: 280.1    BIC: 304.4    (Smaller is better.)

Youth are not more popular

Effect not significant
Worse AIC, BIC

Race-based Homophily

Categorical attribute

ga.base.d1.age.race <- ergm(ga.net~edges + nodematch("sex") + degree(1) + absdiff("birthyear") + nodematch("race"), control = control.ergm(MCMC.burnin = 50000, MCMC.interval = 5000, parallel = 4))
mcmc.diagnostics(ga.base.d1.age.race) # How do the diagnostics look?
ga.base.d1.age.race.gof <- gof(ga.base.d1.age.race)
par(mfrow=c(3, 1)) # Set up the plotting window
plot(ga.base.d1.age.race.gof)

Race-based Homophily

Categorical attribute

ga.base.d1.age.race <- ergm(ga.net~edges + nodematch("sex") + degree(1) + absdiff("birthyear") + nodematch("race"), control = control.ergm(MCMC.burnin = 50000, MCMC.interval = 5000, parallel = 4))
mcmc.diagnostics(ga.base.d1.age.race) # How do the diagnostics look?
ga.base.d1.age.race.gof <- gof(ga.base.d1.age.race)
par(mfrow=c(3, 1)) # Set up the plotting window
plot(ga.base.d1.age.race.gof)
plot(simulate(ga.base.d1.age.race), vertex.col=vcol) # Plot a simulated network

Race-based Homophily

summary(ga.base.d1.age.race)
==========================
Summary of model fit
==========================

Monte Carlo MLE Results:
                  Estimate Std. Error MCMC % p-value    
edges             -0.98160    0.40479      0  0.0155 *  
nodematch.sex     -3.28508    0.72837      0  <1e-04 ***
degree1            1.95555    0.44480      0  <1e-04 ***
absdiff.birthyear -0.13297    0.03116      0  <1e-04 ***
nodematch.race     0.82943    0.36020      0  0.0215 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  263.9  on 941  degrees of freedom
 
AIC: 273.9    BIC: 298.1    (Smaller is better.)

Effects comparable to before
Race-based homophily effect significant
Better AIC, worse BIC

Race-Based Differential Homophily

Relax the homogeneity assumption
Possibility for homophily to differ between groups

ga.base.d1.age.racediff <- ergm(ga.net~edges + nodematch("sex")+ degree(1) + absdiff("birthyear") + nodematch("race", diff = TRUE, keep = c(1, 3)), control = control.ergm(MCMC.burnin=50000, MCMC.interval=5000, parallel = 4))
mcmc.diagnostics(ga.base.d1.age.racediff) # Some issues here. We'll ignore solving them for now.
summary(ga.base.d1.age.racediff)

==========================
Summary of model fit
==========================
Monte Carlo MLE Results:
                     Estimate Std. Error MCMC % p-value    
edges                -0.97137    0.38115      0  0.0110 *  
nodematch.sex        -3.32002    0.73217      0  <1e-04 ***
degree1               1.97079    0.43896      0  <1e-04 ***
absdiff.birthyear    -0.13112    0.03027      0  <1e-04 ***
nodematch.race.Black  2.26102    0.74285      0  0.0024 ** 
nodematch.race.White  0.75956    0.35106      0  0.0307 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  259.4  on 940  degrees of freedom 
AIC: 271.4    BIC: 300.5    (Smaller is better.)

Race-Based Differential Homophily

==========================
Summary of model fit
==========================
Monte Carlo MLE Results:
                     Estimate Std. Error MCMC % p-value    
edges                -0.97137    0.38115      0  0.0110 *  
nodematch.sex        -3.32002    0.73217      0  <1e-04 ***
degree1               1.97079    0.43896      0  <1e-04 ***
absdiff.birthyear    -0.13112    0.03027      0  <1e-04 ***
nodematch.race.Black  2.26102    0.74285      0  0.0024 ** 
nodematch.race.White  0.75956    0.35106      0  0.0307 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
     Null Deviance: 1311.4  on 946  degrees of freedom
 Residual Deviance:  259.4  on 940  degrees of freedom 
AIC: 271.4    BIC: 300.5    (Smaller is better.)

Implications

Effects comparable to before
Race-based homophily significant

Homophily stronger among Black characters than White
0.78 vs. 0.45 probability

Better AIC, worse BIC

Other Models

For example

# Are men or women more popular?
ga.base.d1.age.sex <- ergm(ga.net ~ edges + nodematch("sex") + degree(1) + absdiff("birthyear") + nodefactor("sex"), control = control.ergm(MCMC.burnin = 100000, MCMC.interval = 5000))

# Are certain occupational pairings more likely than others?
ga.base.d1.age.rolemix<-ergm(ga.net ~ edges + nodematch("sex") + degree(1) + absdiff("birthyear") + nodemix("position", base = c(3:5, 9, 12:15, 17:21, 24, 27, 28)), control = control.ergm(MCMC.burnin = 50000, MCMC.interval = 5000))

Estimating Network Properties from Ego Network Data

Smith. 2012. "Macrostructure from Microstructure" Sociological Methodology.

Uses the "target.stats" term in ergm and stergm
Simulates network based upon ego network properties, e.g.,

Degree distribution
Degree correlates and demographic properties
Homophily
Transitivity

Can provide confidence intervals on network measurements, e.g.,

Average path length
Number of components
Cohesive subgroups
etc

General Modeling Advice

Start very simple

Select the best models based upon the BIC

Does the model reflect reality?

Plot simulations for quick diagnosis
Compare simulated measurements to the observed

Check MCMC diagnostics when MCMC called

Extensions

Directed networks (same, yet some different terms)
Weighted networks
Bipartite networks (same, yet some different terms)
...
Temporal networks

Separable Temporal Exponential Random Graph Models

(Hanneke and Xing 2007, Krivitsky and Handcock 2014)

Network model at time t can be represented as an ERGM

It is conditioned upon the network at time t - 1
Discrete time

The difference between two time points represents two networks

Tie formation network (Y+)
Tie dissolution network (Y-)
Separablility: if tie formation is independent of tie dissolution

Two separate ERGM formulas for each network

Theoretical Motivation

Does the process of tie dissolution differ from tie formation? If so, what are their respective processes?

Important to Bear in Mind...

The "tie dissolution" mode refers to tie persistence.

Example Data

University Student Network

"Over the past two weeks, who in our class did you communicate with outside of school?"

Universe

Second-year undergraduates
Honors sociological methods class
Classroom size of 22 students

Asked at three time points ~3 weeks apart
Symmetrized
Actor attributes

Study group
Sex

Read the Data

stergm can take three types of data

network object
networkDynamic object
network.list object
List of network objects

library(tergm)
?stergm
?tergm::ergm.terms
?control.stergm
student.nets <- dget("http://pastebin.com/raw.php?i=f0s1DaD9")
summary(student.nets[[1]])
plot(student.nets[[1]], vertex.col = student.nets[[1]] %v% "studygroupcol", label = student.nets[[1]] %v% "vertex.names")
plot(student.nets[[2]], vertex.col = student.nets[[2]] %v% "studygroupcol", label = student.nets[[2]] %v% "vertex.names")
plot(student.nets[[3]], vertex.col = student.nets[[3]] %v% "studygroupcol", label = student.nets[[3]] %v% "vertex.names")

t1

t2

t3

Homophily Model

studmod1 <- stergm(student.nets, formation = ~edges + nodematch("studygroup"), dissolution = ~edges + nodematch("studygroup"), estimate = "CMLE", times = c(1:3))
summary(studmod1)

==============================
Summary of formation model fit 
==============================
Monte Carlo MLE Results:
                     Estimate Std. Error MCMC % p-value    
edges                 -2.3821     0.1941     NA < 1e-04 ***
nodematch.studygroup   1.2120     0.4281     NA 0.00488 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
     Null Deviance: 528.2  on 381  degrees of freedom
 Residual Deviance: 240.4  on 379  degrees of freedom 
AIC: 244.4    BIC: 252.3    (Smaller is better.)

================================
Summary of dissolution model fit
================================
Monte Carlo MLE Results:
                     Estimate Std. Error MCMC % p-value  
edges                  0.2412     0.4029     NA  0.5512  
nodematch.studygroup   1.4118     0.5429     NA  0.0111 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
     Null Deviance: 112.29  on 81  degrees of freedom
 Residual Deviance:  83.67  on 79  degrees of freedom 
AIC: 87.67    BIC: 92.46    (Smaller is better.)

Homophily + Triangle Model

studmod2 <- stergm(student.nets, formation = ~edges + nodematch("studygroup") + triangle, dissolution = ~edges + nodematch("studygroup") + triangle, estimate = "CMLE", times = c(1:3))
mcmc.diagnostics(studmod2) # Some problems here.studmod2 <- stergm(student.nets, formation = ~edges + nodematch("studygroup") + triangle, dissolution = ~edges + nodematch("studygroup") + triangle, estimate = "CMLE", times=c(1:3), control = control.stergm(CMLE.MCMC.interval = 500, CMLE.MCMC.burnin = 50000, parallel = 4))mcmc.diagnostics(studmod2) # Not great on tie formation, but generally better.
studmod2.gof <- gof(studmod2)
par(mfrow = c(1, 3))
plot(studmod2.gof$formation)
plot(studmod2.gof$dissolution)

Homophily + Triangle Tie Formation

Homophily + Triangle Tie Dissolution

Homophily + Triangle Model

Plot one time slice ahead of each wave

par(mfrow = c(1, 3))
plot(simulate.stergm(studmod2, nw.start = student.nets[[1]]), vertex.col = student.nets[[1]] %v% "studygroupcol", label = student.nets[[1]] %v% "vertex.names")
plot(simulate.stergm(studmod2, nw.start = student.nets[[2]]), vertex.col = student.nets[[2]] %v% "studygroupcol", label = student.nets[[2]] %v% "vertex.names")
plot(simulate.stergm(studmod2, nw.start = student.nets[[3]]), vertex.col = student.nets[[3]] %v% "studygroupcol", label = student.nets[[3]] %v% "vertex.names")

Homophily + Triangle Model

Plot three time slices ahead of the final wave

par(mfrow = c(1, 3))studmod2.w3sim <- simulate.stergm(studmod2, nw.start = student.nets[[3]], time.slices = 1)plot(studmod2.w3sim, vertex.col = student.nets[[3]] %v% "studygroupcol", label = student.nets[[3]] %v% "vertex.names")studmod2.w3sim <- simulate.stergm(studmod2, nw.start = as.network(studmod2.w3sim), time.slices = 1)
plot(studmod2.w3sim, vertex.col = student.nets[[3]] %v% "studygroupcol", label = student.nets[[3]] %v% "vertex.names")
studmod2.w3sim <- simulate.stergm(studmod2, nw.start = as.network(studmod2.w3sim), time.slices = 1)
plot(studmod2.w3sim, vertex.col = student.nets[[3]] %v% "studygroupcol", label = student.nets[[3]] %v% "vertex.names")

t4, t5, t6 Simulated from t3

Homophily + Triangle Model

summary(studmod2)

==============================
Summary of formation model fit 
==============================
Formula:   ~edges + nodematch("studygroup") + triangle
Monte Carlo MLE Results:
                     Estimate Std. Error MCMC % p-value    
edges                 -2.6000     0.2794      0  <1e-04 ***
nodematch.studygroup   1.0710     0.4228      0  0.0117 *  
triangle               0.1801     0.1585      0  0.2566    
---
     Null Deviance: 528.2  on 381  degrees of freedom
 Residual Deviance: 239.2  on 378  degrees of freedom 
AIC: 245.2    BIC: 257.1    (Smaller is better.)

================================
Summary of dissolution model fit
================================
Formula:   ~edges + nodematch("studygroup") + triangle
Monte Carlo MLE Results:
                     Estimate Std. Error MCMC % p-value  
edges                 -0.2232     0.4231      0  0.5993  
nodematch.studygroup   0.7851     0.5204      0  0.1355  
triangle               1.0938     0.4417      0  0.0154 *
---
     Null Deviance: 112.29  on 81  degrees of freedom
 Residual Deviance:  77.18  on 78  degrees of freedom 
AIC: 83.18    BIC: 90.36    (Smaller is better.)

Homophily + Triangle Model

Interpretation

AIC, BIC

Worse for formation
Better for dissolution

Homophily by study group

Forms ties
No effect on preserving ties

Triangle

No effect on tie formation
Preserves ties

Goodness of Fit Troubles

Overestimated 0 edgewise shared partners in formation
Underestimated geodesics in dissolution

"Augmented" Model

Changes

Formation model

Drop triangle, add esp(0)

Dissolution model

Drop nodematch(), add twopath

studmod3 <- stergm(student.nets, formation = ~edges + nodematch("studygroup") + esp(0), dissolution = ~edges + triangle + twopath, estimate = "CMLE", times = c(1:3), control = control.stergm(CMLE.MCMC.interval = 500, CMLE.MCMC.burnin = 50000, parallel = 4))
mcmc.diagnostics(studmod3) # Looks okay!
par(mfrow = c(1, 3))
studmod3.w3sim <- simulate.stergm(studmod3, nw.start = student.nets[[3]], time.slices = 1)
plot(studmod3.w3sim, vertex.col = student.nets[[3]] %v% "studygroupcol", label = student.nets[[3]] %v% "vertex.names")
studmod3.w3sim <- simulate.stergm(studmod3, nw.start = as.network(studmod3.w3sim), time.slices = 1)
plot(studmod3.w3sim, vertex.col = student.nets[[3]] %v% "studygroupcol", label = student.nets[[3]] %v% "vertex.names")
studmod3.w3sim <- simulate.stergm(studmod3, nw.start = as.network(studmod3.w3sim), time.slices = 1)
plot(studmod3.w3sim, vertex.col = student.nets[[3]] %v% "studygroupcol", label = student.nets[[3]] %v% "vertex.names")

t4, t5, t6 Simulated from t3

"Augmented" Model

studmod3.gof <- gof(studmod3)
par(mfrow = c(1, 3))
plot(studmod3.gof$formation)
plot(studmod3.gof$dissolution)

"Augmented" Model Formation

"Augmented" Model Dissolution

"Augmented" Model Summary

summary(studmod3)

==============================
Summary of formation model fit 
==============================
Formula:   ~edges + nodematch("studygroup") + esp(0)
Monte Carlo MLE Results:
                     Estimate Std. Error MCMC % p-value    
edges                 -2.4521     0.1854      0 < 1e-04 ***
nodematch.studygroup   1.0978     0.4190      0 0.00915 ** 
esp0                  -0.7903     0.3546      0 0.02641 *  
---
     Null Deviance: 528.2  on 381  degrees of freedom
 Residual Deviance: 234.0  on 378  degrees of freedom 
AIC: 240    BIC: 251.9    (Smaller is better.)

================================
Summary of dissolution model fit
================================
Formula:   ~edges + triangle + twopath
Monte Carlo MLE Results:
         Estimate Std. Error MCMC %  p-value    
edges      1.6963     0.7737      0 0.031326 *  
triangle   1.7505     0.4486      0 0.000201 ***
twopath   -0.3670     0.1376      0 0.009285 ** 
---
     Null Deviance: 112.29  on 81  degrees of freedom
 Residual Deviance:  69.35  on 78  degrees of freedom 
AIC: 75.35    BIC: 82.53    (Smaller is better.)

"Augmented" Model Summary

Interpretation

AIC and BIC improved
Previous effects retained
Tie formation avoids creating edges with 0 shared partners
Two-paths are unlikely to last

Additional Capacities of STERGM

Cross-sectional network modeling

Tie formation, dissolution inferred from a single network
Requires estimate of mean relationship perseverance

Edges' term equals log(mean perseverance - 1)

Estimation of network properties from ego network statistics

See discussion earlier
Requires estimation of mean relationship perseverance

Alternative Approaches to Longitudinal Networks

Siena

Snijders, van de Bunt, Steglich. 2010. Social Networks.

Software: RSiena

Stochastic actor-based models

Models how actors can change their outgoing ties

Network panel data
Closely in line with agent-based models
Can model social behavior in addition to tie formation

Relational Event Models

Butts. 2008. Sociological Methodology.

Software: relevent

Events occur in real time
Events are of a certain type
An event's occurrence can affect the likelihood of future events

Increase or decrease

Tie formation can be one such event
Events can be transitive, accumulate, reciprocal, etc.
Highly useful for communication networks (among others)

networkDynamic

Part of statnet

Visualization of dynamic networks
Storing fully dynamic network data

NetModelsWS20140627

By Benjamin Lind

NetModelsWS20140627

3,163

Benjamin Lind

benjaminlind

p* Models & Network Dynamics

What are Exponential Random Graph (p*) Models?

"Why Model Social Networks?"

Modeling Steps

Formula

Dependence Assumptions

Which Network Statistics to Model?

Software

Software

Example Data

Read the Data

Plot the Data

Hypotheses?

Documentation

Our Baseline Model

Interpretation

Interpretation

Interpretation

Plot a Simulated Model

How is it different?

Goodness of Fit

Additional Parameter

Additional Parameter

Additional Parameter

Assess Fit

Assess Fit

Markov Chain Monte Carlo

Markov Chain Monte Carlo

Markov Chain Monte Carlo

Markov Chain Monte Carlo

Markov Chain Monte Carlo

Markov Chain Monte Carlo

Alternative Theories

Age Homophily

Age Homophily

Age Homophily

Challenge to Age Homophily Hypothesis

Challenge to Age Homophily Hypothesis

Challenge to Age Homophily Hypothesis

Race-based Homophily

Race-based Homophily

Race-based Homophily

Race-Based Differential Homophily

Race-Based Differential Homophily

Other Models

Estimating Network Properties from Ego Network Data

General Modeling Advice

Extensions

Separable Temporal Exponential Random Graph Models

Theoretical Motivation

Important to Bear in Mind...

Example Data

Read the Data

t1

t2

t3

Homophily Model

Homophily + Triangle Model

Homophily + Triangle Tie Formation

Homophily + Triangle Tie Dissolution

Homophily + Triangle Model

Homophily + Triangle Model

t4, t5, t6 Simulated from t3

Homophily + Triangle Model

Homophily + Triangle Model

"Augmented" Model

t4, t5, t6 Simulated from t3

"Augmented" Model

"Augmented" Model Formation

"Augmented" Model Dissolution

"Augmented" Model Summary

"Augmented" Model Summary

Additional Capacities of STERGM

Alternative Approaches to Longitudinal Networks

Siena

Relational Event Models

networkDynamic

NetModelsWS20140627

More from Benjamin Lind

What are Exponential Random Graph (p)* Models?