====== Lab 06 ======
##########################################################################
# You may cite these labs as follows: McFarland, Daniel, Solomon Messing,
# Mike Nowak, and Sean Westwood. 2010. "Social Network Analysis
# Labs in R." Stanford University.
##########################################################################
##########################################################################
# LAB 6 - Blockmodeling Lab
# The point of this lab is to introduce students to blockmodeling
# techniques that call for a metric of structural equivalence, a method
# and rationale for the selection of the number of positions, and then
# a means of summary representation (mean cutoff and reduced graph
# presentation). Students will be shown how to identify positions using
# correlation as a metric of structural equivalence (euclidean distance
# is used in earlier lab), and they will be taught how to identify more
# isomorphic notions of role-position using the triad census. Last, the
# lab calls upon the user to compare positional techniques and come up
# with a rationale for why they settle on one over another.
##########################################################################
# NOTE: if you have trouble because some packages are not installed,
# see lab 1 for instructions on how to install all necessary packages.
###
#1. SETUP
###
library(igraph)
library(sna)
library(triads)
library(psych)
library(nFactors)
library(NetCluster)
# install.packages(c("sna", "NetCluster"))
###
#2. LOADING AND FORMATTING DATA
###
data(studentnets.M182, package = "NetData")
# Reduce to non-zero edges and build a graph object
m182_full_nonzero_edges <- subset(m182_full_data_frame, (friend_tie > 0 | social_tie > 0 | task_tie > 0))
head(m182_full_nonzero_edges)
m182_full <- graph.data.frame(m182_full_nonzero_edges)
summary(m182_full)
# Create sub-graphs based on edge attributes
m182_friend <- delete_edges(m182_full, E(m182_full)[E(m182_full)$friend_tie==0])
summary(m182_friend)
m182_friend[]
m182_social <- delete_edges(m182_full, E(m182_full)[E(m182_full)$social_tie==0])
summary(m182_social)
m182_social[]
m182_task <- delete_edges(m182_full, E(m182_full)[E(m182_full)$task_tie==0])
summary(m182_task)
m182_task[]
# Look at the plots for each sub-graph
pdf("6.1_m182_studentnet_friend_social_task_plots.pdf", width = 10)
par(mfrow = c(1,3))
friend_layout <- layout.fruchterman.reingold(m182_friend)
plot(m182_friend, layout=friend_layout, main = "friend", edge.arrow.size=.5)
social_layout <- layout.fruchterman.reingold(m182_social)
plot(m182_social, layout=social_layout, main = "social", edge.arrow.size=.5)
task_layout <- layout.fruchterman.reingold(m182_task)
plot(m182_task, layout=task_layout, main = "task", edge.arrow.size=.5)
dev.off()
###
# 3. HIERARCHICAL CLUSTERING ON SOCIAL & TASK TIES
###
# We'll use the "task" and "social" sub-graphs together as the
# basis for our structural equivalence methods. First, we'll use
# the task graph to generate an adjacency matrix.
#
# This matrix represents task interactions directed FROM the
# row individual TO the column individual.
m182_task_matrix_row_to_col <- get.adjacency(m182_task, attr='task_tie')
m182_task_matrix_row_to_col
# To operate on a binary graph, simply leave off the "attr"
# parameter:
m182_task_matrix_row_to_col_bin <- get.adjacency(m182_task)
m182_task_matrix_row_to_col_bin
# For this lab, we'll use the valued graph. The next step is to
# concatenate it with its transpose in order to capture both
# incoming and outgoing task interactions.
m182_task_matrix_col_to_row <- t(as.matrix(m182_task_matrix_row_to_col))
m182_task_matrix_col_to_row
m182_task_matrix <- rbind(m182_task_matrix_row_to_col, m182_task_matrix_col_to_row)
m182_task_matrix
# Next, we'll use the same procedure to add social-interaction
# information.
m182_social_matrix_row_to_col <- get.adjacency(m182_social, attr='social_tie')
m182_social_matrix_row_to_col
m182_social_matrix_row_to_col_bin <- get.adjacency(m182_social)
m182_social_matrix_row_to_col_bin
m182_social_matrix_col_to_row <- t(as.matrix(m182_social_matrix_row_to_col))
m182_social_matrix_col_to_row
m182_social_matrix <- rbind(m182_social_matrix_row_to_col, m182_social_matrix_col_to_row)
m182_social_matrix
m182_task_social_matrix <- rbind(m182_task_matrix, m182_social_matrix)
m182_task_social_matrix
# Now we have a single 4n x n matrix that represents both in- and
# out-directed task and social communication. From this, we can
# generate an n x n correlation matrix that shows the degree of
# structural equivalence of each actor in the network.
m182_task_social_cors <- cor(as.matrix(m182_task_social_matrix))
m182_task_social_cors
# To use correlation values in hierarchical NetCluster, they must
# first be coerced into a "dissimilarity structure" using dist().
# We subtract the values from 1 so that they are all greater than
# or equal to 0; thus, highly dissimilar (i.e., negatively
# correlated) actors have higher values.
dissimilarity <- 1 - m182_task_social_cors
m182_task_social_dist <- as.dist(dissimilarity)
m182_task_social_dist
# Note that it is also possible to use dist() directly on the
# matrix. However, since cor() looks at associations between
# columns and dist() looks at associations between rows, it is
# necessary to transpose the matrix first.
#
# A variety of distance metrics are available; Euclidean
# is the default.
#m182_task_social_dist <- dist(t(m182_task_social_matrix))
#m182_task_social_dist
# hclust() performs a hierarchical agglomerative NetCluster
# operation based on the values in the dissimilarity matrix
# yielded by as.dist() above. The standard visualization is a
# dendrogram. By default, hclust() agglomerates clusters via a
# "complete linkakage" algorithm, determining cluster proximity
# by looking at the distance of the two points across clusters
# that are farthest away from one another. This can be changed via
# the "method" parameter.
pdf("6.2_m182_studentnet_social_hclust.pdf")
m182_task_social_hclust <- hclust(m182_task_social_dist)
plot(m182_task_social_hclust)
dev.off()
# cutree() allows us to use the output of hclust() to set
# different numbers of clusters and assign vertices to clusters
# as appropriate. For example:
cutree(m182_task_social_hclust, k=2)
# Now we'll try to figure out the number of clusters that best
# describes the underlying data. To do this, we'll loop through
# all of the possible numbers of clusters (1 through n, where n is
# the number of actors in the network). For each solution
# corresponding to a given number of clusters, we'll use cutree()
# to assign the vertices to their respective clusters
# corresponding to that solution.
#
# From this, we can generate a matrix of within- and between-
# cluster correlations. Thus, when there is one cluster for each
# vertex in the network, the cell values will be identical to the
# observed correlation matrix, and when there is one cluster for
# the whole network, the values will all be equal to the average
# correlation across the observed matrix.
#
# We can then correlate each by-cluster matrix with the observed
# correlation matrix to see how well the by-cluster matrix fits
# the data. We'll store the correlation for each number of
# clusters in a vector, which we can then plot.
# First, we initialize a vector for storing the correlations and
# set a variable for our number of vertices.
clustered_observed_cors = vector()
num_vertices = length(V(m182_task))
# Next, we loop through the different possible cluster
# configurations, produce matrices of within- and between-
# cluster correlations, and correlate these by-cluster matrices
# with the observed correlation matrix.
pdf("6.3_m182_studentnet_task_social_clustered_observed_corrs.pdf")
clustered_observed_cors <-clustConfigurations(num_vertices,m182_task_social_hclust,m182_task_social_cors)
clustered_observed_cors
plot(clustered_observed_cors$correlations)
dev.off()
clustered_observed_cors$correlations
# From a visual inspection of the correlation matrix, we can
# decide on the proper number of clusters in this network.
# For this network, we'll use 4. (Note that the 1-cluster
# solution doesn't appear on the plot because its correlation
# with the observed correlation matrix is undefined.)
num_clusters = 4
clusters <- cutree(m182_task_social_hclust, k = num_clusters)
clusters
cluster_cor_mat <- clusterCorr(m182_task_social_cors,
clusters)
cluster_cor_mat
# Let's look at the correlation between this cluster configuration
# and the observed correlation matrix. This should match the
# corresponding value from clustered_observed_cors above.
gcor(cluster_cor_mat, m182_task_social_cors)
#####################
# Questions:
# (1) What rationale do you have for selecting the number of
# clusters / positions that you do?
#####################
### NOTE ON DEDUCTIVE CLUSTERING
# It's pretty straightforward, using the code above, to explore
# your own deductive NetCluster. Simply supply your own cluster
# vector, where the elements in the vector are in the same order
# as the vertices in the matrix, and the values represent the
# cluster to which each vertex belongs.
#
# For example, if you believed that actors 2, 7, and 8 formed one
# group, actor 16 former another group, and everyone else formed
# a third group, you could represent this as follows:
deductive_clusters = c(1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1,
1, 3)
# You could then examine the fitness of this cluster configuration
# as follows:
deductive_cluster_cor_mat <- generate_cluster_cor_mat(
m182_task_social_cors,
deductive_clusters)
deductive_cluster_cor_mat
gcor(deductive_cluster_cor_mat, m182_task_social_cors)
### END NOTE ON DEDUCTIVE CLUSTERING
# Now we'll use the 4-cluster solution to generate blockmodels,
# using the raw tie data from the underlying task and social
# networks.
# Task valued
task_mean <- mean(as.matrix(m182_task_matrix_row_to_col))
task_mean
task_valued_blockmodel <- blockmodel(as.matrix(m182_task_matrix_row_to_col), clusters)
task_valued_blockmodel
# Task binary
task_density <- graph.density(m182_task)
task_density
task_binary_blockmodel <- blockmodel(as.matrix(m182_task_matrix_row_to_col_bin), clusters)
task_binary_blockmodel
# Social valued
social_mean <- mean(as.matrix(m182_social_matrix_row_to_col))
social_mean
social_valued_blockmodel <- blockmodel(as.matrix(m182_social_matrix_row_to_col), clusters)
social_valued_blockmodel
# Social binary
social_density <- graph.density(m182_social)
social_density
social_binary_blockmodel <- blockmodel(as.matrix(m182_social_matrix_row_to_col_bin), clusters)
social_binary_blockmodel
# We can also permute the network to examine the within- and
# between-cluster correlations.
cluster_cor_mat_per <- permute_matrix(clusters, cluster_cor_mat)
cluster_cor_mat_per
#####################
# Questions:
# (2) What is the story you get from viewing these clusters,
# and their within and between cluster densities on task and
# social interaction? What can you say about M182 from this?
#####################
###
# 4. HIERARCHICAL CLUSTERING ON TRIAD CENSUS
###
# triads package is outdated. Not available for update r versions.
# - hkim
# Another way to think about roles within a network is by looking
# at the triads that each actor belongs to. We can then use
# correlations between triad-type memberships to identify people
# with similar roles regardless of the specific people with whom
# they interact.
# First, we'll generate an individual-level triad census of the
# network using triadcensus() from the triads package.
task_triads <- triadcensus(m182_task)
task_triads
# Next, we'll generate a matrix of correlations between actors
# in the network based on their similarity in triad-type
# membership. Note that the cor() function in R operates on
# columns, not rows, so in order to get correlations between
# the actors in the network we have to transpose it.
m182_task_triad_cors <- cor(t(task_triads))
m182_task_triad_cors
# As above, we can use the correlation matrix to generate a
# dissimilarity structure, which we can then hierarchically
# cluster into groups of similar people.
dissimilarity <- 1 - m182_task_triad_cors
m182_task_triad_dist <- as.dist(dissimilarity)
m182_task_triad_dist
m182_task_triad_hclust <- hclust(m182_task_triad_dist)
pdf("6.4_m182_studentnet_task_triad_hclust.pdf")
plot(m182_task_triad_hclust)
dev.off()
# As above, we'll loop through each possible cluster solution
# and see how well they match the observed matrix of triad-type
# correlations.
clustered_observed_cors = vector()
num_vertices = length(V(m182_task))
pdf("6.5_m182_studentnet_task_hclust_triad_corrs.pdf")
clustered_observed_cors <-clustConfigurations(num_vertices,m182_task_triad_hclust,m182_task_triad_cors)
dev.off()
clustered_observed_cors
# From a visual inspection of the data, we'll use a 3-cluster
# solution (though a case could also be made for using 5.)
num_clusters = 3
clusters <- cutree(m182_task_triad_hclust, k = num_clusters)
clusters
cluster_cor_mat <- clusterCorr (m182_task_triad_cors,
clusters)
cluster_cor_mat
gcor(cluster_cor_mat, m182_task_triad_cors)
# As before, we can use these clusters to run a blockmodel
# analysis using the underlying tie data from the task network.
# Task valued
task_mean <- mean(m182_task_matrix_row_to_col)
task_mean
task_valued_blockmodel <- blockmodel(m182_task_matrix_row_to_col, clusters)
task_valued_blockmodel
# Task binary
task_density <- graph.density(m182_task)
task_density
task_binary_blockmodel <- blockmodel(m182_task_matrix_row_to_col_bin, clusters)
task_binary_blockmodel
# Finally, we can try to get a sense of what our different
# clusters represent by generating a cluster-by-triad-type matrix.
# This is an m x n matrix, where m is the number of clusters and n
# is the 36 possible triad types. Each cell is the average
# number of the given triad type for each individual in the
# cluster.
cluster_triad_mat <- matrix(nrow=max(clusters), ncol=ncol(task_triads))
for (i in 1:max(clusters)) {
for (j in 1:ncol(task_triads)) {
cluster_triad_mat[i,j] <- mean(task_triads[which(clusters==i),j])
}
}
cluster_triad_mat
#####################
# Questions:
# (3) What does clustering of the triadic census afford us?
# What roles do you see? Redo the initial blockmodel analysis
# without social interaction (only task) and then compare to
# this solution. Do they differ?
#
# Extra credit: Try running the triad census on task AND
# social interaction separately and then correlating persons.
# What result do you get? Is it different from our initial
# blockmodel result? Show your code.
######################
###
# 5. FACTOR ANALYSIS
###
# Note that although we are conducting a principal components
# analysis (PCA), which is technically not exactly the same as
# factor analysis, we will use the term "factor" to describe the
# individual components in our PCA.
# PCA is often used in network analysis as a form of detecting
# individuals global positioning. We say "global" because these
# clusters aren't defined on local cohesion but from the overall
# pattern of ties individuals have with all others (structural
# equivalence). Identifying the first two largest components that
# organize the variance in tie patterns is one way of doing this.
# We'll analyze the 4n x n matrix generated above.
# First, we want to determine the ideal number of components
# (factors) to extract. We'll do this by examining the eigenvalues
# in a scree plot and examining how each number of factors stacks
# up to a few proposed non-graphical solutions to selecting the
# optimal number of components, available via the nFactors
# package.
ev <- eigen(cor(as.matrix(m182_task_social_matrix))) # get eigenvalues
ap <- parallel(subject=nrow(m182_task_social_matrix),
var=ncol(m182_task_social_matrix),
rep=100,cent=.05)
nS <- nScree(ev$values, ap$eigen$qevpea)
pdf("6.6_m182_studentnet_task_social_pca_scree.pdf")
plotnScree(nS)
# To draw a line across the graph where eigenvalues are = 1,
# use the following code:
plotnScree(nS)
abline(h=1)
dev.off()
# For more information on this procedure, please see
# the references provided in the parallel() documentation
# (type "?parallel" in the R command line with the package
# loaded).
# Now we'll run a principal components analysis on the matrix,
# using the number of factors determined above (note this may not
# be the same number as you get):
pca_m182_task_social = principal(as.matrix(m182_task_social_matrix), nfactors=5, rotate="varimax")
# Let's take a look at the results in the R terminal:
pca_m182_task_social
# You can see the standardized loadings for each factor for each
# node. Note that R sometimes puts the factors in a funky order
# (e.g. RC1, RC2, RC5, RC4, RC3) but all of the factors are there.
# You can see that the SS loadings, proportion of variance
# explained and cumulative variance explained is provided below. A
# Chi Square test of the factors and various other statistics are
# provided below.
# Note that the eigenvalues can be accessed via the following
# command:
pca_m182_task_social$values
# Now we will use the factor loadings to cluster and compare that
# to our other NetCluster techniques, using dendrograms.
# Take the distance based on Euclidian Distance
m182_task_factor_dist = dist(pca_m182_task_social$loadings)
# And cluster
m182_task_factor_hclust <- hclust(m182_task_factor_dist)
pdf("6.7_m182_studentnet_task_social_pca_hclust.pdf")
plot(m182_task_factor_hclust)
dev.off()
# And compare to NetCluster based on correlations and triads:
pdf("6.8_m182_task_cluster_by_correlation_PCA_Triads.pdf")
par(mfrow = c(1,3))
plot(m182_task_social_hclust, main = "Correlation")
plot(m182_task_factor_hclust, main = "PCA")
plot(m182_task_triad_hclust, main = "Triads")
dev.off()
#####################
# Questions:
# (4) How do the results across blockmodel techniques differ?
# Why might you use one over the other? Why might you want to
# run more than one in your analyses?
#####################
====== Lab 06: Step 4 deleted ======
##########################################################################
# You may cite these labs as follows: McFarland, Daniel, Solomon Messing,
# Mike Nowak, and Sean Westwood. 2010. "Social Network Analysis
# Labs in R." Stanford University.
##########################################################################
##########################################################################
# LAB 6 - Blockmodeling Lab
# The point of this lab is to introduce students to blockmodeling
# techniques that call for a metric of structural equivalence, a method
# and rationale for the selection of the number of positions, and then
# a means of summary representation (mean cutoff and reduced graph
# presentation). Students will be shown how to identify positions using
# correlation as a metric of structural equivalence (euclidean distance
# is used in earlier lab), and they will be taught how to identify more
# isomorphic notions of role-position using the triad census. Last, the
# lab calls upon the user to compare positional techniques and come up
# with a rationale for why they settle on one over another.
##########################################################################
# NOTE: if you have trouble because some packages are not installed,
# see lab 1 for instructions on how to install all necessary packages.
###
#1. SETUP
###
library(igraph)
library(sna)
library(triads)
library(psych)
library(nFactors)
library(NetCluster)
###
#2. LOADING AND FORMATTING DATA
###
data(studentnets.M182, package = "NetData")
# Reduce to non-zero edges and build a graph object
m182_full_nonzero_edges <- subset(m182_full_data_frame, (friend_tie > 0 | social_tie > 0 | task_tie > 0))
head(m182_full_nonzero_edges)
m182_full <- graph.data.frame(m182_full_nonzero_edges)
summary(m182_full)
# Create sub-graphs based on edge attributes
m182_friend <- delete_edges(m182_full, E(m182_full)[E(m182_full)$friend_tie==0])
summary(m182_friend)
m182_social <- delete_edges(m182_full, E(m182_full)[E(m182_full)$social_tie==0])
summary(m182_social)
m182_task <- delete_edges(m182_full, E(m182_full)[E(m182_full)$task_tie==0])
summary(m182_task)
# Look at the plots for each sub-graph
pdf("6.1_m182_studentnet_friend_social_task_plots.pdf", width = 10)
par(mfrow = c(1,3))
friend_layout <- layout.fruchterman.reingold(m182_friend)
plot(m182_friend, layout=friend_layout, main = "friend", edge.arrow.size=.5)
social_layout <- layout.fruchterman.reingold(m182_social)
plot(m182_social, layout=social_layout, main = "social", edge.arrow.size=.5)
task_layout <- layout.fruchterman.reingold(m182_task)
plot(m182_task, layout=task_layout, main = "task", edge.arrow.size=.5)
dev.off()
###
# 3. HIERARCHICAL CLUSTERING ON SOCIAL & TASK TIES
###
# We'll use the "task" and "social" sub-graphs together as the
# basis for our structural equivalence methods. First, we'll use
# the task graph to generate an adjacency matrix.
#
# This matrix represents task interactions directed FROM the
# row individual TO the column individual.
m182_task_matrix_row_to_col <- get.adjacency(m182_task, attr='task_tie')
m182_task_matrix_row_to_col
# To operate on a binary graph, simply leave off the "attr"
# parameter:
m182_task_matrix_row_to_col_bin <- get.adjacency(m182_task)
m182_task_matrix_row_to_col_bin
# For this lab, we'll use the valued graph. The next step is to
# concatenate it with its transpose in order to capture both
# incoming and outgoing task interactions.
m182_task_matrix_col_to_row <- t(as.matrix(m182_task_matrix_row_to_col))
m182_task_matrix_col_to_row
m182_task_matrix <- rbind(m182_task_matrix_row_to_col, m182_task_matrix_col_to_row)
m182_task_matrix
# Next, we'll use the same procedure to add social-interaction
# information.
m182_social_matrix_row_to_col <- get.adjacency(m182_social, attr='social_tie')
m182_social_matrix_row_to_col
m182_social_matrix_row_to_col_bin <- get.adjacency(m182_social)
m182_social_matrix_row_to_col_bin
m182_social_matrix_col_to_row <- t(as.matrix(m182_social_matrix_row_to_col))
m182_social_matrix_col_to_row
m182_social_matrix <- rbind(m182_social_matrix_row_to_col, m182_social_matrix_col_to_row)
m182_social_matrix
m182_task_social_matrix <- rbind(m182_task_matrix, m182_social_matrix)
m182_task_social_matrix
# Now we have a single 4n x n matrix that represents both in- and
# out-directed task and social communication. From this, we can
# generate an n x n correlation matrix that shows the degree of
# structural equivalence of each actor in the network.
m182_task_social_cors <- cor(as.matrix(m182_task_social_matrix))
m182_task_social_cors
# To use correlation values in hierarchical NetCluster, they must
# first be coerced into a "dissimilarity structure" using dist().
# We subtract the values from 1 so that they are all greater than
# or equal to 0; thus, highly dissimilar (i.e., negatively
# correlated) actors have higher values.
dissimilarity <- 1 - m182_task_social_cors
m182_task_social_dist <- as.dist(dissimilarity)
m182_task_social_dist
# Note that it is also possible to use dist() directly on the
# matrix. However, since cor() looks at associations between
# columns and dist() looks at associations between rows, it is
# necessary to transpose the matrix first.
#
# A variety of distance metrics are available; Euclidean
# is the default.
#m182_task_social_dist <- dist(t(m182_task_social_matrix))
#m182_task_social_dist
# hclust() performs a hierarchical agglomerative NetCluster
# operation based on the values in the dissimilarity matrix
# yielded by as.dist() above. The standard visualization is a
# dendrogram. By default, hclust() agglomerates clusters via a
# "complete linkakage" algorithm, determining cluster proximity
# by looking at the distance of the two points across clusters
# that are farthest away from one another. This can be changed via
# the "method" parameter.
pdf("6.2_m182_studentnet_social_hclust.pdf")
m182_task_social_hclust <- hclust(m182_task_social_dist)
plot(m182_task_social_hclust)
dev.off()
# cutree() allows us to use the output of hclust() to set
# different numbers of clusters and assign vertices to clusters
# as appropriate. For example:
cutree(m182_task_social_hclust, k=2)
# Now we'll try to figure out the number of clusters that best
# describes the underlying data. To do this, we'll loop through
# all of the possible numbers of clusters (1 through n, where n is
# the number of actors in the network). For each solution
# corresponding to a given number of clusters, we'll use cutree()
# to assign the vertices to their respective clusters
# corresponding to that solution.
#
# From this, we can generate a matrix of within- and between-
# cluster correlations. Thus, when there is one cluster for each
# vertex in the network, the cell values will be identical to the
# observed correlation matrix, and when there is one cluster for
# the whole network, the values will all be equal to the average
# correlation across the observed matrix.
#
# We can then correlate each by-cluster matrix with the observed
# correlation matrix to see how well the by-cluster matrix fits
# the data. We'll store the correlation for each number of
# clusters in a vector, which we can then plot.
# First, we initialize a vector for storing the correlations and
# set a variable for our number of vertices.
clustered_observed_cors = vector()
num_vertices = length(V(m182_task))
# Next, we loop through the different possible cluster
# configurations, produce matrices of within- and between-
# cluster correlations, and correlate these by-cluster matrices
# with the observed correlation matrix.
pdf("6.3_m182_studentnet_task_social_clustered_observed_corrs.pdf")
clustered_observed_cors <-clustConfigurations(num_vertices,m182_task_social_hclust,m182_task_social_cors)
clustered_observed_cors
plot(clustered_observed_cors$correlations)
dev.off()
clustered_observed_cors$correlations
# From a visual inspection of the correlation matrix, we can
# decide on the proper number of clusters in this network.
# For this network, we'll use 4. (Note that the 1-cluster
# solution doesn't appear on the plot because its correlation
# with the observed correlation matrix is undefined.)
num_clusters = 4
clusters <- cutree(m182_task_social_hclust, k = num_clusters)
clusters
cluster_cor_mat <- clusterCorr(m182_task_social_cors,
clusters)
cluster_cor_mat
# Let's look at the correlation between this cluster configuration
# and the observed correlation matrix. This should match the
# corresponding value from clustered_observed_cors above.
gcor(cluster_cor_mat, m182_task_social_cors)
#####################
# Questions:
# (1) What rationale do you have for selecting the number of
# clusters / positions that you do?
#####################
### NOTE ON DEDUCTIVE CLUSTERING
# It's pretty straightforward, using the code above, to explore
# your own deductive NetCluster. Simply supply your own cluster
# vector, where the elements in the vector are in the same order
# as the vertices in the matrix, and the values represent the
# cluster to which each vertex belongs.
#
# For example, if you believed that actors 2, 7, and 8 formed one
# group, actor 16 former another group, and everyone else formed
# a third group, you could represent this as follows:
deductive_clusters = c(1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1,
1, 3)
# You could then examine the fitness of this cluster configuration
# as follows:
deductive_cluster_cor_mat <- generate_cluster_cor_mat(
m182_task_social_cors,
deductive_clusters)
deductive_cluster_cor_mat
gcor(deductive_cluster_cor_mat, m182_task_social_cors)
### END NOTE ON DEDUCTIVE CLUSTERING
# Now we'll use the 4-cluster solution to generate blockmodels,
# using the raw tie data from the underlying task and social
# networks.
# Task valued
task_mean <- mean(as.matrix(m182_task_matrix_row_to_col))
task_mean
task_valued_blockmodel <- blockmodel(as.matrix(m182_task_matrix_row_to_col), clusters)
task_valued_blockmodel
# Task binary
task_density <- graph.density(m182_task)
task_density
task_binary_blockmodel <- blockmodel(as.matrix(m182_task_matrix_row_to_col_bin), clusters)
task_binary_blockmodel
# Social valued
social_mean <- mean(as.matrix(m182_social_matrix_row_to_col))
social_mean
social_valued_blockmodel <- blockmodel(as.matrix(m182_social_matrix_row_to_col), clusters)
social_valued_blockmodel
# Social binary
social_density <- graph.density(m182_social)
social_density
social_binary_blockmodel <- blockmodel(as.matrix(m182_social_matrix_row_to_col_bin), clusters)
social_binary_blockmodel
# We can also permute the network to examine the within- and
# between-cluster correlations.
cluster_cor_mat_per <- permute_matrix(clusters, cluster_cor_mat)
cluster_cor_mat_per
#####################
# Questions:
# (2) What is the story you get from viewing these clusters,
# and their within and between cluster densities on task and
# social interaction? What can you say about M182 from this?
#####################
#
# 4. deleted.
#
#####################
# Questions:
# (3) What does clustering of the triadic census afford us?
# What roles do you see? Redo the initial blockmodel analysis
# without social interaction (only task) and then compare to
# this solution. Do they differ?
#
# Extra credit: Try running the triad census on task AND
# social interaction separately and then correlating persons.
# What result do you get? Is it different from our initial
# blockmodel result? Show your code.
######################
###
# 5. FACTOR ANALYSIS
###
# Note that although we are conducting a principal components
# analysis (PCA), which is technically not exactly the same as
# factor analysis, we will use the term "factor" to describe the
# individual components in our PCA.
# PCA is often used in network analysis as a form of detecting
# individuals global positioning. We say "global" because these
# clusters aren't defined on local cohesion but from the overall
# pattern of ties individuals have with all others (structural
# equivalence). Identifying the first two largest components that
# organize the variance in tie patterns is one way of doing this.
# We'll analyze the 4n x n matrix generated above.
# First, we want to determine the ideal number of components
# (factors) to extract. We'll do this by examining the eigenvalues
# in a scree plot and examining how each number of factors stacks
# up to a few proposed non-graphical solutions to selecting the
# optimal number of components, available via the nFactors
# package.
ev <- eigen(cor(as.matrix(m182_task_social_matrix))) # get eigenvalues
ap <- parallel(subject=nrow(m182_task_social_matrix),
var=ncol(m182_task_social_matrix),
rep=100,cent=.05)
nS <- nScree(ev$values, ap$eigen$qevpea)
pdf("6.6_m182_studentnet_task_social_pca_scree.pdf")
plotnScree(nS)
# To draw a line across the graph where eigenvalues are = 1,
# use the following code:
plotnScree(nS)
abline(h=1)
dev.off()
# For more information on this procedure, please see
# the references provided in the parallel() documentation
# (type "?parallel" in the R command line with the package
# loaded).
# Now we'll run a principal components analysis on the matrix,
# using the number of factors determined above (note this may not
# be the same number as you get):
pca_m182_task_social = principal(as.matrix(m182_task_social_matrix), nfactors=5, rotate="varimax")
# Let's take a look at the results in the R terminal:
pca_m182_task_social
# You can see the standardized loadings for each factor for each
# node. Note that R sometimes puts the factors in a funky order
# (e.g. RC1, RC2, RC5, RC4, RC3) but all of the factors are there.
# You can see that the SS loadings, proportion of variance
# explained and cumulative variance explained is provided below. A
# Chi Square test of the factors and various other statistics are
# provided below.
# Note that the eigenvalues can be accessed via the following
# command:
pca_m182_task_social$values
# Now we will use the factor loadings to cluster and compare that
# to our other NetCluster techniques, using dendrograms.
# Take the distance based on Euclidian Distance
m182_task_factor_dist = dist(pca_m182_task_social$loadings)
# And cluster
m182_task_factor_hclust <- hclust(m182_task_factor_dist)
pdf("6.7_m182_studentnet_task_social_pca_hclust.pdf")
plot(m182_task_factor_hclust)
dev.off()
# And compare to NetCluster based on correlations and triads:
pdf("6.8_m182_task_cluster_by_correlation_PCA_Triads.pdf")
par(mfrow = c(1,3))
plot(m182_task_social_hclust, main = "Correlation")
plot(m182_task_factor_hclust, main = "PCA")
plot(m182_task_triad_hclust, main = "Triads")
dev.off()
#####################
# Questions:
# (4) How do the results across blockmodel techniques differ?
# Why might you use one over the other? Why might you want to
# run more than one in your analyses?
#####################
====== no pdf ======
##########################################################################
# You may cite these labs as follows: McFarland, Daniel, Solomon Messing,
# Mike Nowak, and Sean Westwood. 2010. "Social Network Analysis
# Labs in R." Stanford University.
##########################################################################
##########################################################################
# LAB 6 - Blockmodeling Lab
# The point of this lab is to introduce students to blockmodeling
# techniques that call for a metric of structural equivalence, a method
# and rationale for the selection of the number of positions, and then
# a means of summary representation (mean cutoff and reduced graph
# presentation). Students will be shown how to identify positions using
# correlation as a metric of structural equivalence (euclidean distance
# is used in earlier lab), and they will be taught how to identify more
# isomorphic notions of role-position using the triad census. Last, the
# lab calls upon the user to compare positional techniques and come up
# with a rationale for why they settle on one over another.
##########################################################################
# NOTE: if you have trouble because some packages are not installed,
# see lab 1 for instructions on how to install all necessary packages.
###
#1. SETUP
###
library(igraph)
library(sna)
library(triads)
library(psych)
library(nFactors)
library(NetCluster)
###
#2. LOADING AND FORMATTING DATA
###
data(studentnets.M182, package = "NetData")
# Reduce to non-zero edges and build a graph object
m182_full_nonzero_edges <- subset(m182_full_data_frame, (friend_tie > 0 | social_tie > 0 | task_tie > 0))
head(m182_full_nonzero_edges)
m182_full <- graph.data.frame(m182_full_nonzero_edges)
summary(m182_full)
# Create sub-graphs based on edge attributes
m182_friend <- delete_edges(m182_full, E(m182_full)[E(m182_full)$friend_tie==0])
summary(m182_friend)
m182_social <- delete_edges(m182_full, E(m182_full)[E(m182_full)$social_tie==0])
summary(m182_social)
m182_task <- delete_edges(m182_full, E(m182_full)[E(m182_full)$task_tie==0])
summary(m182_task)
# Look at the plots for each sub-graph
# pdf("6.1_m182_studentnet_friend_social_task_plots.pdf", width = 10)
par(mfrow = c(1,3))
friend_layout <- layout.fruchterman.reingold(m182_friend)
plot(m182_friend, layout=friend_layout, main = "friend", edge.arrow.size=.5)
social_layout <- layout.fruchterman.reingold(m182_social)
plot(m182_social, layout=social_layout, main = "social", edge.arrow.size=.5)
task_layout <- layout.fruchterman.reingold(m182_task)
plot(m182_task, layout=task_layout, main = "task", edge.arrow.size=.5)
# dev.off()
# back to normal window pannel (no partition)
par(mfrow = c(1,1))
###
# 3. HIERARCHICAL CLUSTERING ON SOCIAL & TASK TIES
###
# We'll use the "task" and "social" sub-graphs together as the
# basis for our structural equivalence methods. First, we'll use
# the task graph to generate an adjacency matrix.
#
# This matrix represents task interactions directed FROM the
# row individual TO the column individual.
m182_task_matrix_row_to_col <- get.adjacency(m182_task, attr='task_tie')
m182_task_matrix_row_to_col
# To operate on a binary graph, simply leave off the "attr"
# parameter:
m182_task_matrix_row_to_col_bin <- get.adjacency(m182_task)
m182_task_matrix_row_to_col_bin
# For this lab, we'll use the valued graph. The next step is to
# concatenate it with its transpose in order to capture both
# incoming and outgoing task interactions.
m182_task_matrix_col_to_row <- t(as.matrix(m182_task_matrix_row_to_col))
m182_task_matrix_col_to_row
m182_task_matrix <- rbind(m182_task_matrix_row_to_col, m182_task_matrix_col_to_row)
m182_task_matrix
# Next, we'll use the same procedure to add social-interaction
# information.
m182_social_matrix_row_to_col <- get.adjacency(m182_social, attr='social_tie')
m182_social_matrix_row_to_col
m182_social_matrix_row_to_col_bin <- get.adjacency(m182_social)
m182_social_matrix_row_to_col_bin
m182_social_matrix_col_to_row <- t(as.matrix(m182_social_matrix_row_to_col))
m182_social_matrix_col_to_row
m182_social_matrix <- rbind(m182_social_matrix_row_to_col, m182_social_matrix_col_to_row)
m182_social_matrix
m182_task_social_matrix <- rbind(m182_task_matrix, m182_social_matrix)
m182_task_social_matrix
# Now we have a single 4n x n matrix that represents both in- and
# out-directed task and social communication. From this, we can
# generate an n x n correlation matrix that shows the degree of
# structural equivalence of each actor in the network.
m182_task_social_cors <- cor(as.matrix(m182_task_social_matrix))
m182_task_social_cors
# To use correlation values in hierarchical NetCluster, they must
# first be coerced into a "dissimilarity structure" using dist().
# We subtract the values from 1 so that they are all greater than
# or equal to 0; thus, highly dissimilar (i.e., negatively
# correlated) actors have higher values.
dissimilarity <- 1 - m182_task_social_cors
m182_task_social_dist <- as.dist(dissimilarity)
m182_task_social_dist
# Note that it is also possible to use dist() directly on the
# matrix. However, since cor() looks at associations between
# columns and dist() looks at associations between rows, it is
# necessary to transpose the matrix first.
#
# A variety of distance metrics are available; Euclidean
# is the default.
#m182_task_social_dist <- dist(t(m182_task_social_matrix))
#m182_task_social_dist
# hclust() performs a hierarchical agglomerative NetCluster
# operation based on the values in the dissimilarity matrix
# yielded by as.dist() above. The standard visualization is a
# dendrogram. By default, hclust() agglomerates clusters via a
# "complete linkakage" algorithm, determining cluster proximity
# by looking at the distance of the two points across clusters
# that are farthest away from one another. This can be changed via
# the "method" parameter.
# pdf("6.2_m182_studentnet_social_hclust.pdf")
m182_task_social_hclust <- hclust(m182_task_social_dist)
plot(m182_task_social_hclust)
# dev.off()
# cutree() allows us to use the output of hclust() to set
# different numbers of clusters and assign vertices to clusters
# as appropriate. For example:
cutree(m182_task_social_hclust, k=2)
# Now we'll try to figure out the number of clusters that best
# describes the underlying data. To do this, we'll loop through
# all of the possible numbers of clusters (1 through n, where n is
# the number of actors in the network). For each solution
# corresponding to a given number of clusters, we'll use cutree()
# to assign the vertices to their respective clusters
# corresponding to that solution.
#
# From this, we can generate a matrix of within- and between-
# cluster correlations. Thus, when there is one cluster for each
# vertex in the network, the cell values will be identical to the
# observed correlation matrix, and when there is one cluster for
# the whole network, the values will all be equal to the average
# correlation across the observed matrix.
#
# We can then correlate each by-cluster matrix with the observed
# correlation matrix to see how well the by-cluster matrix fits
# the data. We'll store the correlation for each number of
# clusters in a vector, which we can then plot.
# First, we initialize a vector for storing the correlations and
# set a variable for our number of vertices.
clustered_observed_cors = vector()
num_vertices = length(V(m182_task))
# Next, we loop through the different possible cluster
# configurations, produce matrices of within- and between-
# cluster correlations, and correlate these by-cluster matrices
# with the observed correlation matrix.
# pdf("6.3_m182_studentnet_task_social_clustered_observed_corrs.pdf")
clustered_observed_cors <-clustConfigurations(num_vertices,m182_task_social_hclust,m182_task_social_cors)
clustered_observed_cors
plot(clustered_observed_cors$correlations)
# dev.off()
clustered_observed_cors$correlations
# From a visual inspection of the correlation matrix, we can
# decide on the proper number of clusters in this network.
# For this network, we'll use 4. (Note that the 1-cluster
# solution doesn't appear on the plot because its correlation
# with the observed correlation matrix is undefined.)
num_clusters = 4
clusters <- cutree(m182_task_social_hclust, k = num_clusters)
clusters
cluster_cor_mat <- clusterCorr(m182_task_social_cors,
clusters)
cluster_cor_mat
# Let's look at the correlation between this cluster configuration
# and the observed correlation matrix. This should match the
# corresponding value from clustered_observed_cors above.
gcor(cluster_cor_mat, m182_task_social_cors)
#####################
# Questions:
# (1) What rationale do you have for selecting the number of
# clusters / positions that you do?
#####################
### NOTE ON DEDUCTIVE CLUSTERING
# It's pretty straightforward, using the code above, to explore
# your own deductive NetCluster. Simply supply your own cluster
# vector, where the elements in the vector are in the same order
# as the vertices in the matrix, and the values represent the
# cluster to which each vertex belongs.
#
# For example, if you believed that actors 2, 7, and 8 formed one
# group, actor 16 former another group, and everyone else formed
# a third group, you could represent this as follows:
deductive_clusters = c(1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1,
1, 3)
# You could then examine the fitness of this cluster configuration
# as follows:
deductive_cluster_cor_mat <- generate_cluster_cor_mat(
m182_task_social_cors,
deductive_clusters)
deductive_cluster_cor_mat
gcor(deductive_cluster_cor_mat, m182_task_social_cors)
### END NOTE ON DEDUCTIVE CLUSTERING
# Now we'll use the 4-cluster solution to generate blockmodels,
# using the raw tie data from the underlying task and social
# networks.
# Task valued
task_mean <- mean(as.matrix(m182_task_matrix_row_to_col))
task_mean
task_valued_blockmodel <- blockmodel(as.matrix(m182_task_matrix_row_to_col), clusters)
task_valued_blockmodel
# Task binary
task_density <- graph.density(m182_task)
task_density
task_binary_blockmodel <- blockmodel(as.matrix(m182_task_matrix_row_to_col_bin), clusters)
task_binary_blockmodel
# Social valued
social_mean <- mean(as.matrix(m182_social_matrix_row_to_col))
social_mean
social_valued_blockmodel <- blockmodel(as.matrix(m182_social_matrix_row_to_col), clusters)
social_valued_blockmodel
# Social binary
social_density <- graph.density(m182_social)
social_density
social_binary_blockmodel <- blockmodel(as.matrix(m182_social_matrix_row_to_col_bin), clusters)
social_binary_blockmodel
# We can also permute the network to examine the within- and
# between-cluster correlations.
cluster_cor_mat_per <- permute_matrix(clusters, cluster_cor_mat)
cluster_cor_mat_per
#####################
# Questions:
# (2) What is the story you get from viewing these clusters,
# and their within and between cluster densities on task and
# social interaction? What can you say about M182 from this?
#####################
#
# 4. deleted.
#
#####################
# Questions:
# (3) What does clustering of the triadic census afford us?
# What roles do you see? Redo the initial blockmodel analysis
# without social interaction (only task) and then compare to
# this solution. Do they differ?
#
# Extra credit: Try running the triad census on task AND
# social interaction separately and then correlating persons.
# What result do you get? Is it different from our initial
# blockmodel result? Show your code.
######################
###
# 5. FACTOR ANALYSIS
###
# Note that although we are conducting a principal components
# analysis (PCA), which is technically not exactly the same as
# factor analysis, we will use the term "factor" to describe the
# individual components in our PCA.
# PCA is often used in network analysis as a form of detecting
# individuals global positioning. We say "global" because these
# clusters aren't defined on local cohesion but from the overall
# pattern of ties individuals have with all others (structural
# equivalence). Identifying the first two largest components that
# organize the variance in tie patterns is one way of doing this.
# We'll analyze the 4n x n matrix generated above.
# First, we want to determine the ideal number of components
# (factors) to extract. We'll do this by examining the eigenvalues
# in a scree plot and examining how each number of factors stacks
# up to a few proposed non-graphical solutions to selecting the
# optimal number of components, available via the nFactors
# package.
ev <- eigen(cor(as.matrix(m182_task_social_matrix))) # get eigenvalues
ap <- parallel(subject=nrow(m182_task_social_matrix),
var=ncol(m182_task_social_matrix),
rep=100,cent=.05)
nS <- nScree(ev$values, ap$eigen$qevpea)
# pdf("6.6_m182_studentnet_task_social_pca_scree.pdf")
plotnScree(nS)
# To draw a line across the graph where eigenvalues are = 1,
# use the following code:
plotnScree(nS)
abline(h=1)
# dev.off()
# For more information on this procedure, please see
# the references provided in the parallel() documentation
# (type "?parallel" in the R command line with the package
# loaded).
# Now we'll run a principal components analysis on the matrix,
# using the number of factors determined above (note this may not
# be the same number as you get):
pca_m182_task_social = principal(as.matrix(m182_task_social_matrix), nfactors=5, rotate="varimax")
# Let's take a look at the results in the R terminal:
pca_m182_task_social
# You can see the standardized loadings for each factor for each
# node. Note that R sometimes puts the factors in a funky order
# (e.g. RC1, RC2, RC5, RC4, RC3) but all of the factors are there.
# You can see that the SS loadings, proportion of variance
# explained and cumulative variance explained is provided below. A
# Chi Square test of the factors and various other statistics are
# provided below.
# Note that the eigenvalues can be accessed via the following
# command:
pca_m182_task_social$values
# Now we will use the factor loadings to cluster and compare that
# to our other NetCluster techniques, using dendrograms.
# Take the distance based on Euclidian Distance
m182_task_factor_dist = dist(pca_m182_task_social$loadings)
# And cluster
m182_task_factor_hclust <- hclust(m182_task_factor_dist)
# pdf("6.7_m182_studentnet_task_social_pca_hclust.pdf")
plot(m182_task_factor_hclust)
# dev.off()
# And compare to NetCluster based on correlations and triads:
# pdf("6.8_m182_task_cluster_by_correlation_PCA_Triads.pdf")
par(mfrow = c(1,2))
plot(m182_task_social_hclust, main = "Correlation")
plot(m182_task_factor_hclust, main = "PCA")
# the below one that didn't work
# plot(m182_task_triad_hclust, main = "Triads")
# dev.off()
#####################
# Questions:
# (4) How do the results across blockmodel techniques differ?
# Why might you use one over the other? Why might you want to
# run more than one in your analyses?
#####################