[statnet_help] Assistance with Calculating Degree Centrality in Directed and Valued Networks Using the sna Package in R

[CHU-DING LING via statnet_help]

Dear Carter,



Thanks again for your suggestions. I have some further questions regarding
inconsistencies between the Bonacich’s power centrality (Beta centrality)
from *igraph* and *sna* for an undirected and valued network. Enclosed
please find the example codes and the associated annotations. Despite
setting the ‘exponent’ and ‘rescale’ parameters consistently between the
two packages, I am noticing some discrepancies in the results.



I have a couple of questions which I was hoping you could help with:



*1. How can we get the highest beta value before calculating Bonacich’s
power centrality?* Currently I use another unpublished package to obtain
the value. I would like to understand the process or any specific
considerations required to obtain the highest beta value with *sna* or
*igraph*.



*2. How can we achieve consistent results between ‘igraph’ and ‘sna’?*
Despite aligning the ‘exponent’ and ‘rescale’ parameters across both
packages, the results still differ. Are there any additional parameters or
factors I should consider to ensure the outputs are consistent?



I would greatly appreciate any guidance or insights you could provide on
these matters.



Thank you for your time and assistance.



Best regards,

Chuding


Carter T. Butts via statnet_help <statnet_help at u.washington.edu>
于2024年8月30日周五 16:00写道：


> Hi, Chuding -

>

> Glad that fixed it.   With respect to edgelists vs. adjacency matrices,

> they will give you equivalent results.  If you seem to be getting different

> results, check the two objects: somewhere in your code, you've presumably

> made them non-equivalent....

>

> Best,

>

> -Carter

> On 8/26/24 5:59 AM, CHU-DING LING wrote:

>

> Carter,

>

>

> Thank you for your suggestions! The problem has been resolved.

>

>

>

> Initially, I imported a matrix from a CSV file and stored it as a matrix

> class object. I then converted it into a network class object since many

> functions in *sna* require objects to be of the network class. However, I

> noticed that the edge weights were lost during the conversion from the

> matrix object to the network object, which caused the results from the

> degree() function not to account for edge weights.

>

>

>

> Actually, the degree() function can directly handle the matrix object. I

> also used as.sociomatrix.sna() to convert the original matrix object into

> another matrix object with a different name. Both approaches produced the

> same degree centrality results for the directed and valued network.

>

>

>

> I also experimented with the as.edgelist.sna() function to convert the

> original matrix object into an edgelist object. However, when I calculated

> the degree centrality of this object, it produced incorrect results, with a

> greater number of elements than the number of nodes in my network. I

> appreciate if you can give some insights on this issue.

>

>

>

> Thanks in advance!

>

>

>

> Chuding

>

> Carter T. Butts via statnet_help <statnet_help at u.washington.edu>

> 于2024年8月25日周日 05:15写道：

>

>> H, Chuding -

>>

>> The degree() function already exploits edge values; this is its default

>> behavior.  If you wish to *ignore* edge values, you need to set the

>> "ignore.eval" argument to TRUE.

>>

>> If you are not getting valued degree calculations from degree() using the

>> defaults, then you are not passing it valued data.  This may be due to a

>> preprocessing error (so check your inputs).  Another possible failure mode

>> is that you are passing it a network object that has value information

>> stored as an edge attribute, and are expecting degree() to use those edge

>> values.  Since a network object can have any number of edge attributes (or

>> none at all), and they can be of any data type (i.e., not necessarily

>> numeric), degree() can't automagically know what is intended in that case,

>> and will therefore treat the data as unvalued.  An easy way to use edge

>> attribute information is to wrap your object in a call like

>> as.edgelist.sna(<mynet>,attrname=<whateveredgeattributeIwanttouse>), which

>> will extract from the object the specific valued network that you want to

>> analyze.  That's especially handy if you have several different edge values

>> you want to store in the same network object.  Of course, you can also use

>> that same trick to make a "working" edgelist at the top of your script that

>> you reuse for multiple calculations.  (The same can be done with adjacency

>> matrices rather than edgelists, if one prefers.  See e.g.

>> ?as.sociomatrix.sna.)

>>

>> Hope that helps,

>>

>> -Carter

>> On 8/23/24 9:07 PM, CHU-DING LING via statnet_help wrote:

>>

>> Dear all,

>>

>>

>>

>> I hope this message finds you well. I am currently working on a project

>> that involves social network analysis using the *sna* package in R. I am

>> reaching out to seek your expertise on a particular issue I have

>> encountered regarding the calculation of degree centrality in directed and

>> valued networks.

>>

>>

>>

>> I am working with a directed network where edges have associated weights.

>> My goal is to accurately calculate both the in-degree and out-degree

>> centrality of nodes while considering the edge weights. I attempted to

>> calculate the degree centrality using the degree function in the *sna*

>> package. While this function works well for unweighted networks, I realized

>> that it does not account for edge weights.

>>

>>

>>

>> Could you please advise on the best method or function within the *sna*

>> package to accurately calculate the degree centrality in this context?

>> Though I can make it with *igraph* or other packages, I am particularly

>> interested in whether *sna* could directly handle weighted edges in

>> directed networks.

>>

>>

>>

>> Your guidance would be invaluable, and I would greatly appreciate any

>> suggestions or resources you might be able to provide. Thank you for your

>> time and consideration. I look forward to your insights.

>>

>>

>>

>> Best,

>>

>> Chuding

>>

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## Step 1: Create a 5*5 undirected and valued matrix

set.seed(123)  # Set seed for reproducibility
matrix_values <- sample(1:4, 15, replace = TRUE)  # Generate random weights for the upper triangle
undirected_matrix <- matrix(0, 5, 5)  # Initialize a 5x5 matrix

# Fill the upper triangle with random values

undirected_matrix[upper.tri(undirected_matrix)] <- matrix_values

# Make the matrix symmetric to represent an undirected graph

undirected_matrix <- undirected_matrix + t(undirected_matrix)

undirected_matrix

## Step 2: Convert the matrix to an igraph object

library(igraph)

undirected_matrix_graph <- graph_from_adjacency_matrix(undirected_matrix, 
                                                 mode = c("undirected"), 
                                                 weighted = TRUE, 
                                                 diag = FALSE)

## Step 3: Calculate Bonacich's power centrality (Beta centrality)

# 0.1025942 in below specification is the highest Beta value 
# from the results of running xBetaCentrality() in xUCINET package.
# I do not know how to obtain the highest Beta value with igraph or sna.

Results_from_igraph <- power_centrality(
  undirected_matrix_graph,
  nodes = V(undirected_matrix_graph),
  loops = FALSE,
  exponent = 0.1025942,
  rescale = FALSE,
  tol = 1e-07,
  sparse = TRUE
)

Results_from_igraph <- data.frame(Results_from_igraph = Results_from_igraph)

library(sna)

Results_from_sna <- bonpow(undirected_matrix,
                   gmode="graph",
                   diag=FALSE,
                   tmaxdev=FALSE,
                   exponent=0.1025942,
                   rescale=FALSE,
                   tol=1e-07)

Results_from_sna <- data.frame(Results_from_sna = Results_from_sna)

## Step 4: Combine the two results and compare them

# The values in 'Results_from_igraph' show no variance, 
# which differs from the observation with my own dataset. 
# However, with my own dataset, 
# the results are still not consistent with those in 'Results_from_sna'.

CombinedResults <- cbind(Results_from_igraph, Results_from_sna)

CombinedResults

cor(CombinedResults)