r - igraph中的子组件失败(实验2的后半部分)

Question

我已经开始探索斯坦福大学的社交网络分析类(class)，并且在第二个实验室中，我遇到了所提供代码的问题。这是代码的链接:http://sna.stanford.edu/sna_R_labs/lab_2.R

它涉及以下功能:

####################################################################
# LAB 2: Methodological beginnings - Density, Reciprocity, Triads, #
# Transitivity, and heterogeneity. Node and network statistics.    #
####################################################################


# NOTE: if you have trouble because some packages are not installed, 
# see lab 1 for instructions on how to install all necessary packages. 
# Also see Lab 1 for prior functions.


##############################################################
# 
# Lab 2 
#
# The purpose of this lab is to acquire basic cohesion 
# metrics of density, reciprocity, reach, path distance, 
# and transitivity. In addition, we'll develop triadic 
# analyses and a measure of ego-network heterogenity. 
#
##############################################################



### 
# 1. SET UP SESSION
###
install.packages("NetData")

library(igraph)
library(NetData)
library(sna)

### 
# 2. LOAD DATA
###

# We would ordinarily need to follow the same proceedure we did for the Krackhardt data
# as we did in lab 1; see that lab for detail.

data(kracknets, package = "NetData")
data(package = "NetData")
# Reduce to non-zero edges and build a graph object
krack_full_nonzero_edges <- subset(krack_full_data_frame, (advice_tie > 0 | friendship_tie > 0 | reports_to_tie > 0))
head(krack_full_nonzero_edges)

krack_full <- graph.data.frame(krack_full_nonzero_edges) 
summary(krack_full)

# Set vertex attributes
for (i in V(krack_full)) {
  for (j in names(attributes)) {
    krack_full <- set.vertex.attribute(krack_full, j, index=i, attributes[i+1,j])
  }
}
summary(krack_full)

# Create sub-graphs based on edge attributes
krack_advice <- delete.edges(krack_full, E(krack_full)[get.edge.attribute(krack_full,name = "advice_tie")==0])
summary(krack_advice)

krack_friendship <- delete.edges(krack_full, E(krack_full)[get.edge.attribute(krack_full,name = "friendship_tie")==0])
summary(krack_friendship)

krack_reports_to <- delete.edges(krack_full, E(krack_full)[get.edge.attribute(krack_full,name = "reports_to_tie")==0])
summary(krack_reports_to)


### 
# 3. NODE-LEVEL STATISTICS
###

# Compute the indegree and outdegree for each node, first in the 
# full graph (accounting for all tie types) and then in each 
# tie-specific sub-graph. 
deg_full_in <- degree(krack_full, mode="in") 
deg_full_out <- degree(krack_full, mode="out") 
deg_full_in
deg_full_out

deg_advice_in <- degree(krack_advice, mode="in") 
deg_advice_out <- degree(krack_advice, mode="out") 
deg_advice_in
deg_advice_out

deg_friendship_in <- degree(krack_friendship, mode="in") 
deg_friendship_out <- degree(krack_friendship, mode="out") 
deg_friendship_in
deg_friendship_out

deg_reports_to_in <- degree(krack_reports_to, mode="in") 
deg_reports_to_out <- degree(krack_reports_to, mode="out") 
deg_reports_to_in
deg_reports_to_out

# Reachability can only be computed on one vertex at a time. To
# get graph-wide statistics, change the value of "vertex"
# manually or write a for loop. (Remember that, unlike R objects,
# igraph objects are numbered from 0.)

reachability <- function(g, m) {
  reach_mat = matrix(nrow = vcount(g), 
                     ncol = vcount(g))
  for (i in 1:vcount(g)) {
    reach_mat[i,] = 0
    this_node_reach <- subcomponent(g, i, mode = m) # used "i" instead of "(i - 1)"

    for (j in 1:(length(this_node_reach))) {
      alter = this_node_reach[j] # removed "+ 1"
      reach_mat[i, alter] = 1
    }
   }
  return(reach_mat)
}

reach_full_in <- reachability(krack_full, 'in')
reach_full_out <- reachability(krack_full, 'out')
reach_full_in
reach_full_out

reach_advice_in <- reachability(krack_advice, 'in')
reach_advice_out <- reachability(krack_advice, 'out')
reach_advice_in
reach_advice_out

reach_friendship_in <- reachability(krack_friendship, 'in')
reach_friendship_out <- reachability(krack_friendship, 'out')
reach_friendship_in
reach_friendship_out

reach_reports_to_in <- reachability(krack_reports_to, 'in')
reach_reports_to_out <- reachability(krack_reports_to, 'out')
reach_reports_to_in
reach_reports_to_out


# Often we want to know path distances between individuals in a network. 
# This is often done by calculating geodesics, or shortest paths between
# each ij pair. One can symmetrize the data to do this (see lab 1), or 
# calculate it for outward and inward ties separately. Averaging geodesics 
# for the entire network provides an average distance or sort of cohesiveness
# score. Dichotomizing distances reveals reach, and an average of reach for 
# a network reveals what percent of a network is connected in some way.

# Compute shortest paths between each pair of nodes. 
sp_full_in <- shortest.paths(krack_full, mode='in')
sp_full_out <- shortest.paths(krack_full, mode='out')
sp_full_in
sp_full_out

sp_advice_in <- shortest.paths(krack_advice, mode='in')
sp_advice_out <- shortest.paths(krack_advice, mode='out')
sp_advice_in
sp_advice_out

sp_friendship_in <- shortest.paths(krack_friendship, mode='in')
sp_friendship_out <- shortest.paths(krack_friendship, mode='out')
sp_friendship_in
sp_friendship_out

sp_reports_to_in <- shortest.paths(krack_reports_to, mode='in')
sp_reports_to_out <- shortest.paths(krack_reports_to, mode='out')
sp_reports_to_in
sp_reports_to_out


# Assemble node-level stats into single data frame for export as CSV.

# First, we have to compute average values by node for reachability and
# shortest path. (We don't have to do this for degree because it is 
# already expressed as a node-level value.)
reach_full_in_vec <- vector()
reach_full_out_vec <- vector()
reach_advice_in_vec <- vector()
reach_advice_out_vec <- vector()
reach_friendship_in_vec <- vector()
reach_friendship_out_vec <- vector()
reach_reports_to_in_vec <- vector()
reach_reports_to_out_vec <- vector()

sp_full_in_vec <- vector()
sp_full_out_vec <- vector()
sp_advice_in_vec <- vector()
sp_advice_out_vec <- vector()
sp_friendship_in_vec <- vector()
sp_friendship_out_vec <- vector()
sp_reports_to_in_vec <- vector()
sp_reports_to_out_vec <- vector()

for (i in 1:vcount(krack_full)) {
  reach_full_in_vec[i] <- mean(reach_full_in[i,])
  reach_full_out_vec[i] <- mean(reach_full_out[i,])
  reach_advice_in_vec[i] <- mean(reach_advice_in[i,])
  reach_advice_out_vec[i] <- mean(reach_advice_out[i,])
  reach_friendship_in_vec[i] <- mean(reach_friendship_in[i,])
  reach_friendship_out_vec[i] <- mean(reach_friendship_out[i,])
  reach_reports_to_in_vec[i] <- mean(reach_reports_to_in[i,])
  reach_reports_to_out_vec[i] <- mean(reach_reports_to_out[i,])

  sp_full_in_vec[i] <- mean(sp_full_in[i,])
  sp_full_out_vec[i] <- mean(sp_full_out[i,])
  sp_advice_in_vec[i] <- mean(sp_advice_in[i,])
  sp_advice_out_vec[i] <- mean(sp_advice_out[i,])
  sp_friendship_in_vec[i] <- mean(sp_friendship_in[i,])
  sp_friendship_out_vec[i] <- mean(sp_friendship_out[i,])
  sp_reports_to_in_vec[i] <- mean(sp_reports_to_in[i,])
  sp_reports_to_out_vec[i] <- mean(sp_reports_to_out[i,])
}

# Next, we assemble all of the vectors of node-levelvalues into a 
# single data frame, which we can export as a CSV to our working
# directory.
node_stats_df <- cbind(deg_full_in,
                       deg_full_out,
                       deg_advice_in,
                       deg_advice_out,
                       deg_friendship_in,
                       deg_friendship_out,
                       deg_reports_to_in,
                       deg_reports_to_out, 

                       reach_full_in_vec, 
                       reach_full_out_vec, 
                       reach_advice_in_vec, 
                       reach_advice_out_vec, 
                       reach_friendship_in_vec, 
                       reach_friendship_out_vec, 
                       reach_reports_to_in_vec, 
                       reach_reports_to_out_vec, 

                       sp_full_in_vec, 
                       sp_full_out_vec, 
                       sp_advice_in_vec, 
                       sp_advice_out_vec, 
                       sp_friendship_in_vec, 
                       sp_friendship_out_vec, 
                       sp_reports_to_in_vec, 
                       sp_reports_to_out_vec)

write.csv(node_stats_df, 'krack_node_stats.csv')

# Question #1 - What do these statistics tell us about
# each network and its individuals in general? 

### 
# 3. NETWORK-LEVEL STATISTICS
###

# Many initial analyses of networks begin with distances and reach, 
# and then move towards global summary statistics of the network. 
#
# As a reminder, entering a question mark followed by a function 
# name (e.g., ?graph.density) pulls up the help file for that function.
# This can be helpful to understand how, exactly, stats are calculated.

# Degree
mean(deg_full_in)
sd(deg_full_in)
mean(deg_full_out)
sd(deg_full_out)

mean(deg_advice_in)
sd(deg_advice_in)
mean(deg_advice_out)
sd(deg_advice_out)

mean(deg_friendship_in)
sd(deg_friendship_in)
mean(deg_friendship_out)
sd(deg_friendship_out)

mean(deg_reports_to_in)
sd(deg_reports_to_in)
mean(deg_reports_to_out)
sd(deg_reports_to_out)


# Shortest paths
# ***Why do in and out come up with the same results?
# In and out shortest paths are simply transposes of one another; 
# thus, when we compute statistics across the whole network they have to be the same.

mean(sp_full_in[which(sp_full_in != Inf)])
sd(sp_full_in[which(sp_full_in != Inf)])
mean(sp_full_out[which(sp_full_out != Inf)])
sd(sp_full_out[which(sp_full_out != Inf)])

mean(sp_advice_in[which(sp_advice_in != Inf)])
sd(sp_advice_in[which(sp_advice_in != Inf)])
mean(sp_advice_out[which(sp_advice_out != Inf)])
sd(sp_advice_out[which(sp_advice_out != Inf)])

mean(sp_friendship_in[which(sp_friendship_in != Inf)])
sd(sp_friendship_in[which(sp_friendship_in != Inf)])
mean(sp_friendship_out[which(sp_friendship_out != Inf)])
sd(sp_friendship_out[which(sp_friendship_out != Inf)])

mean(sp_reports_to_in[which(sp_reports_to_in != Inf)])
sd(sp_reports_to_in[which(sp_reports_to_in != Inf)])
mean(sp_reports_to_out[which(sp_reports_to_out != Inf)])
sd(sp_reports_to_out[which(sp_reports_to_out != Inf)])

# Reachability
mean(reach_full_in[which(reach_full_in != Inf)])
sd(reach_full_in[which(reach_full_in != Inf)])
mean(reach_full_out[which(reach_full_out != Inf)])
sd(reach_full_out[which(reach_full_out != Inf)])

mean(reach_advice_in[which(reach_advice_in != Inf)])
sd(reach_advice_in[which(reach_advice_in != Inf)])
mean(reach_advice_out[which(reach_advice_out != Inf)])
sd(reach_advice_out[which(reach_advice_out != Inf)])

mean(reach_friendship_in[which(reach_friendship_in != Inf)])
sd(reach_friendship_in[which(reach_friendship_in != Inf)])
mean(reach_friendship_out[which(reach_friendship_out != Inf)])
sd(reach_friendship_out[which(reach_friendship_out != Inf)])

mean(reach_reports_to_in[which(reach_reports_to_in != Inf)])
sd(reach_reports_to_in[which(reach_reports_to_in != Inf)])
mean(reach_reports_to_out[which(reach_reports_to_out != Inf)])
sd(reach_reports_to_out[which(reach_reports_to_out != Inf)])

# Density 
graph.density(krack_full)
graph.density(krack_advice)
graph.density(krack_friendship)
graph.density(krack_reports_to)

# Reciprocity
reciprocity(krack_full)
reciprocity(krack_advice)
reciprocity(krack_friendship)
reciprocity(krack_reports_to)

# Transitivity
transitivity(krack_full)
transitivity(krack_advice)
transitivity(krack_friendship)
transitivity(krack_reports_to)

# Triad census. Here we'll first build a vector of labels for 
# the different triad types. Then we'll combine this vector
# with the triad censuses for the different networks, which 
# we'll export as a CSV.

census_labels = c('003',
                  '012',
                  '102',
                 '021D',
                 '021U',
                 '021C',
                 '111D',
                 '111U',
                 '030T',
                 '030C',
                  '201',
                 '120D',
                 '120U',
                 '120C',
                  '210',
                  '300')
tc_full <- triad.census(krack_full)
tc_advice <- triad.census(krack_advice)
tc_friendship <- triad.census(krack_friendship)
tc_reports_to <- triad.census(krack_reports_to)

triad_df <- data.frame(census_labels,
                   tc_full, 
                   tc_advice, 
                   tc_friendship,
                   tc_reports_to)
triad_df

# To export any of these vectors to a CSV for use in another program, simply
# use the write.csv() command:
write.csv(triad_df, 'krack_triads.csv')

# Question #2 - (a) How do the three networks differ on network statictics? 
# (b) What does the triad census tell us? Can you calculate the likelihood of
# any triad's occurrence? (c) See the back of Wasserman and Faust and its section
# on triads. Calculate the degree of clustering and hierarchy in Excel. 
# What do we learn from that?




### 
# 4. HETEROGENEITY 
###

# Miller and McPherson write about processes of homophily and
# here we take a brief look at one version of this issue. 
# In particular, we look at the extent to which each actor's
# "associates" (friend, advisor, boos) are heterogenous or not.

# We'll use a statistic called the IQV, or Index of Qualitative
# Variation. This is just an implementation of Blau's Index of
# Heterogeneity (known to economists as the Herfindahl-Hirschman
# index), normalized so that perfect heterogeneity (i.e., equal 
# distribution across categories) equals 1.

# NOTE that this code only works with categorical variables that 
# have been numerically coded to integer values that ascend
# sequentially from 0; you may have to recode your data to get this
# to work properly.
# We are interested in many of the attributes of nodes.  To save 
# time and to make our lives better we are going to create a function
# that will provide an IQV statistic for any network and for
# any categorical variable.  A function is a simple way to
# create code that is both reusable and easier to edit.

# Functions have names and receive arguments.  For example,
# anytime you call table() you are calling the table function.
# We could write code to duplicate the table function for each
# of our variables, but it is faster to write a single general tool
# that will provide frequencies for any variable. If I have
# a dataframe with the variable gender and I want to see the
# split of males and females I would pass the argument
# "dataframe$gender" to the table function. We follow a
# similar model here. Understanding each step is less important
# than understanding the usefulness and power of functions.

get_iqvs <- function(graph, attribute) {

  #we have now defined a function, get_iqvs, that will take the
  # graph "graph" and find the iqv statistic for the categorical
  # variable "attribute." Within this function whenever we use the 
  #variables graph or attribute they correspond to the graph and
  # variable we passed (provided) to the function

  mat <- get.adjacency(graph)

  # To make this function work on a wide variety of variables we
  # find out how many coded levels (unique responses) exist for
  # the attribute variable programatically

  attr_levels = get.vertex.attribute(graph,
                                     attribute,
                                     V(graph))

  num_levels = length(unique(attr_levels))
  iqvs = rep(0, nrow(mat))

  # Now that we know how many levels exist we want to loop
  # (go through) each actor in the network. Loops iterate through
  # each value in a range.  Here we are looking through each ego
  # in the range of egos starting at the first and ending at the
  # last.  The function nrow provides the number of rows in an
  # object and the ":" opperand specifies the range.  Between
  # the curly braces of the for loop ego will represent exactly
  # one value between 1 and the number of rows in the graph
  # object, iterating by one during each execution of the loop.

  for (ego in 1:nrow(mat)) {

    # initialize actor-specific variables
    alter_attr_counts = rep(0, num_levels)
    num_alters_this_ego = 0
    sq_fraction_sum = 0

    # For each ego we want to check each tied alter for the same
    # level on the variable attribute as the ego.

    for (alter in 1:ncol(mat)) {

      # only examine alters that are actually tied to ego
      if (mat[ego, alter] == 1) {

        num_alters_this_ego = num_alters_this_ego + 1

        # get the alter's level on the attribute 
        alter_attr = get.vertex.attribute(graph, 
                                      attribute, (alter - 1))

        # increment the count of alters with this level
        # of the attribute by 1
        alter_attr_counts[alter_attr + 1] =
          alter_attr_counts[alter_attr + 1] + 1
      }
     }

    # now that we're done looping through all of the alters,
    # get the squared fraction for each level of the attribute
    # out of the total number of attributes
    for (i in 1:num_levels) {
      attr_fraction = alter_attr_counts[i] /
        num_alters_this_ego
      sq_fraction_sum = sq_fraction_sum + attr_fraction ^ 2
     }

     # now we can compute the ego's blau index...
     blau_index = 1 - sq_fraction_sum

     # and the ego's IQV, which is just a normalized blau index
     iqvs[ego] = blau_index / (1 - (1 / num_levels))
   }

   # The final part of a function returns the calculated value.
   #  So if we called get_iqvs(testgraph, gender) return would
   # provide the iqvs for gender in the test graph.  If we are also
   # intersted in race we could simply change the function call
   # to get_iqvs(testgraph, race).  No need to write all this
   # code again for different variables.

   return(iqvs)
 }



 # For this data set, we'll look at homophily across departments, 
 # which is already coded 0-4, so no recoding is needed. 

advice_iqvs <- get_iqvs(krack_advice, 'DEPT')
advice_iqvs

friendship_iqvs <- get_iqvs(krack_friendship, 'DEPT')
friendship_iqvs

reports_to_iqvs <- get_iqvs(krack_reports_to, 'DEPT')
reports_to_iqvs

# Question #3 - What does the herfindahl index reveal about 
# attribute sorting in networks? What does it mean for each network?


#####
# Extra-credit: What might be a better way to test the occurrence 
# of homophily or segregation in a network? How might we code that in R?
#####

#####
# Tau statistic (code by Sam Pimentel)
#####


#R code for generating random graphs:
#requires packages ergm, intergraph

#set up weighting vectors for clustering and hierarchy
clust.mask <- rep(0,16)
clust.mask[c(1,3,16)] <- 1
hier.mask <- rep(1,16)
hier.mask[c(6:8,10:11)] <- 0

 #compute triad count and triad proportion for a given weighting vector
mask.stat <- function(my.graph, my.mask){
n.nodes <- vcount(my.graph)
n.edges <- ecount(my.graph)
#set probability of edge formation in random graph to proportion of possibleedges present in original
p.edge <- n.edges/(n.nodes*(n.nodes +1)/2)
r.graph <- as.network.numeric(n.nodes, density = p.edge)
r.igraph <- as.igraph(r.graph)
tc.graph <- triad.census(r.igraph)
clust <- sum(tc.graph*my.mask)
clust.norm <- clust/sum(tc.graph)
return(c(clust,clust.norm))
}
install.packages('intergraph')
library(intergraph)
#build 100 random graphs and compute their clustering and hierarchy measurements to create an empirical null distribution
emp.distro <- function(this.graph){
  clust <- matrix(rep(0,200), nrow=2)
  hier <- matrix(rep(0,200),nrow=2)
  for(i in c(1:100)){
    clust[,i] <- mask.stat(this.graph, clust.mask)
    hier[,i] <- mask.stat(this.graph, hier.mask)
  }
  my.mat <- rbind(clust, hier)
  rownames(my.mat) <- c("clust.ct", "clust.norm", "hier.ct", "hier.ct.norm")
  return(my.mat)
}


#fix randomization if desired so results are replicable
set.seed(3123)
#compute empirical distributions for each network
hc_advice <- emp.distro(krack_advice) 
hc_friend <- emp.distro(krack_friendship)
hc_report <- emp.distro(krack_reports_to)
When I try and run the hc_advice variable, this error comes up: 

hc_advice <- emp.distro(krack_advice)
Error in mask.stat(this.graph, clust.mask) : 
  could not find function "as.network.numeric"

我不确定是什么问题，因为我完全按照说明进行操作，并且我认为as.network.numeric与此无关。

任何帮助将不胜感激!

谢谢

Answer 1

您是否已安装并加载ergm软件包？ as.network.numeric来自那里。

编辑:好的，我已经检查了network包，似乎as.network.numeric不见了。但是，as.network的文档说:

If the ‘ergm’ package is loaded, ‘network’ can function as a shorthand for ‘as.network.numeric’ if ‘x’ is an integer specifying the number of nodes.

看来它仍然有效:

> as.network(100, density=0.002)
 Network attributes:
  vertices = 100
  directed = TRUE
  hyper = FALSE
  loops = FALSE
  multiple = FALSE
  bipartite = FALSE
  total edges= 24
    missing edges= 0
    non-missing edges= 24

 Vertex attribute names:
    vertex.names

因此，我认为您可以简单地将 as.network.numeric替换为 as.network，它应该可以工作。