Dear Chuding,
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Szabolcs
On Sat, 6 Jun 2020 at 16:23, CHU-DING LING <lingchud...@gmail.com> wrote:
> Dear all,
>
>
>
> I am trying to calculate the betweenness centrality with valued ties, but
> I do not figure it out. Here are the dataset and codes.
>
>
>
> 1. This is an example dataset with only four nodes/individuals. When
> collecting the network data, I asked the participants to answer the
> question about friendship tie on a 7-point Likert scale. So, this is a
> *directed* and *valued* network. Moreover, when inputting the network
> data, I adopted the edge list format and saved it into a CSV file. The
> details of the data are as follows:
>
>
>
> Actor Target Friend
>
> 1001 1002 5
>
> 1001 1003 6
>
> 1001 1004 5
>
> 1002 1001 6
>
> 1002 1003 6
>
> 1002 1004 6
>
> 1003 1001 4
>
> 1003 1002 4
>
> 1003 1004 4
>
> 1004 1001 6
>
> 1004 1002 6
>
> 1004 1003 6
>
>
>
> 2. Then I ran the following codes to calculate the betweenness centrality:
>
>
>
> library(igraph)
>
>
>
> #Step 1. read the edgelist format dataset into R
>
> Mydata <- read.table("Example.csv", header=TRUE, sep=",")
>
>
>
> #Step 2. convert an edgelist matrix with valued edges/ties into a graph
>
> Mygraph <- graph_from_data_frame(Mydata, directed=TRUE)
>
>
>
> #Step 3. calculate betweenness centrality but fail to account for the
> value/weight of the tie
>
> betweenness (Mygraph, directed = T, normalized = T)
>
>
>
> 3. The results came out are as follows:
>
>
>
> 1001 1002 1003 1004
>
> 0 0 0 0
>
>
>
> It seems that the package treated the dataset as that the four members
> mutually nominated each other as friends while ignored the strength of the
> tie. Therefore, each of them was connected to the others and no one had the
> opportunity to be a broker.
>
>
>
> I have searched archival of the list, but I failed to locate the
> information that can completely solve my problem. So, I am wondering
> whether any colleagues here could share with me any information about this.
> I would be grateful if you can provide me any suggestions or references.
> Many thanks in advance!
>
>
>
> Best,
>
> Chuding
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