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

[CHU-DING LING via statnet_help]

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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