I am not aware of any general literature on this topic. You could have a look at the Fernandez and Gould-paper that does something similar with brokerage measures. They define – based on directionality, which your data should have – various roles based on ego networks.

Best,
Tore

Do u think i can just assign roles based on three centrality? for example,
make the one with big centralites as a leader. Centrality can be the measure of influence within the community?

Thanks in advance,
MJ

You can look at my paper called Clustering in Weighted Networks for determining the level of clustering / cliqueness in a network. But if you would like to find the specific cliques or communities, I would suggest you contact some of the community detection guys. Also, you might want to have a look at the book Generalized Blockmodeling by Dorian, Batagelj, and Ferligoj.

Tore

Thanks for your response. As you mentioned before, by copying the iconv.dll file in igraph-libs, it worked.

Can you suggest me something about cliques in weighted graphs. I have seen your related to node centrality and clustering coefficient.
Just wondering, if you have any ideas on finding cliques in weighted graph.

Regard
Nitesh

This was due to a problem with the igraph-package. The guys behind that package (Gabor Csardi) have just submitted a new version of their R-package that should solve this issue. As soon as it has been approved by CRAN and disseminated across the servers, you should be able to get it by writing update.packages() in R.

Tore

The igraph-guys (Gabor Csardi) have just submitted a new version of their R-package that should solve this issue. As soon as it has been approved by CRAN and disseminated across the servers, you should be able to get it by writing update.packages() in R.

Tore

Nitesh

Thanks for taking an interest in tnet. This is an issue with the igraph-package that tnet depends on. You can fix it by copying the iconv.dll from a previous version of igraph (or email me). I have sent them a bug report – hopefully they will have it sorted soon.

Best,
Tore

Great work!!

Just having problem in installing tnet package in R 2.11.1.

The error I am getting is

Loading required package: igraph
Error : .onLoad failed in loadNamespace() for ‘igraph’, details:
call: inDL(x, as.logical(local), as.logical(now), …)
error: unable to load shared library ‘C:/PROGRA~1/R/R-211~1.1/library/igraph/libs/igraph.dll’:
LoadLibrary failure: The specified module could not be found.

Error: package ‘igraph’ could not be loaded

In the pop window, it is showing that iconv.dll file was not found..

Can you help me please??

Thank you for taking an interest in my work, and tnet.

tnet requires that igraph is installed as this package does some of these computationally heavy stuff in C++ and tnet is solely written in R.

Did you install tnet using the install.packages(“tnet”)-command? If so, igraph should have automatiically been installed. Also, try to run update.packages() to see whether an update could sort the packages out. If none of these things work, please contact me offline.

Best,

Tore

I installed R on my pc andI installed the package tnet.
If i compute your code, R give me an error:
“Loading required package: igraph
Error : .onLoad failed in loadNamespace() for ‘igraph’, details:
call: inDL(x, as.logical(local), as.logical(now), …)
error: unable to load shared library ‘C:/Users/ssuweis/Documents/R/win-library/2.11/igraph/libs/igraph.dll’:
LoadLibrary failure: The specified module could not be found.”

So I installed also the package “igraph” and I tried to load it, but a new error appear:

“Error: package/namespace load failed for ‘igraph’”

Of course I have set the directory in the folder where I have my “personal library”‘, so I cannot understand why I cannot load both tnet and igraph package.

I think it is a trivial problem that occurs to beginners in R like me, andI tried to google the latter error, but I did not find any useful suggestions..If you can help I really appreciate it!

Thanks and best

Samir

You need to include alpha in the measure-parameter.

Tore

#Load tnet and network
library(tnet)
net <- cbind(
i=c(1,1,2,2,2,2,3,3,4,5,5,6),
j=c(2,3,1,3,4,5,1,2,2,2,6,5),
w=c(4,2,4,4,1,2,2,4,1,2,1,1))  

# Run function
degree_w(net, measure=c("degree", "output", "alpha"), alpha=0.5)

# Output
     node degree output    alpha
[1,]    1      2      6 3.464102
[2,]    2      4     11 6.633250
[3,]    3      2      6 3.464102
[4,]    4      1      1 1.000000
[5,]    5      2      3 2.449490
[6,]    6      1      1 1.000000

I’m trying to calculate the degree_w for my data, but it seems that the output is always equivalent when alpha is set as 1. Even for the sample network in this post, I got the same results when varying the alpha parameter…
Please, could you tell me what is my mistake here?
With many thanks

Mauricio

> degree_w(net,measure=c(“degree”,”output”), type=”out”, alpha=1)
node degree output
[1,] 1 2 6
[2,] 2 4 11
[3,] 3 2 6
[4,] 4 1 1
[5,] 5 2 3
[6,] 6 1 1
> degree_w(net,measure=c(“degree”,”output”), type=”out”, alpha=0.5)
node degree output
[1,] 1 2 6
[2,] 2 4 11
[3,] 3 2 6
[4,] 4 1 1
[5,] 5 2 3
[6,] 6 1 1

That entirely depend on your raw data. R is great for manipulating data: have a look at Quick-R, http://www.statmethods.net/, for an introduction.

Best,
Tore

I have actually made those in illustrator / photoshop. Not recommended, but that method enables you to make the graphs exactly as you want.

Best,
Tore

What kind of software do you use for drawing your graph pictures?

For example, this one:

http://thetore.files.wordpress.com/2008/12/fig1.png?w=271&h=177

Thanks,
Ben

got a quick question, maybe you can help me: I’m calculating the EigenValues of a Matrix, but R provides me only ordered values..is there a way to have unordered results?

Thnaks in advance

Marco

It worked. Thanks a lot.

Thor

Tore

I am trying to use tnet for a behavioral dataset as a trial, I tried an undirected network .

sampledata <- rbind(
c(1,2,3),
c(1,3,2),
c(2,3,4),
c(2,4,5),
c(2,5,2),
c(6,5,1))

and used the weighted_richclub_w() function on the above. I find that for directed=FALSE and for rich="k" and reshuffle="weights", it gives the following error message:

"Error in random.m[i, j + 1] <- random.phi[which(random.phi[, "x"] == j), :
subscript out of bounds"

The same error appears for rich="k" and reshuffle="links" with directed=FALSE

With directed=NULL there is no error !

1. I don't understand why it gave an error with directed=FALSE even though the input was actually an undirected network.

2. For the case of directed weighted network, how is the generalization done for null models weight reshuffle and weights & links reshuffle of your PRL 101 paper ?

3. Does the combination rich="k", reshuffle="links" correspond to weight reshuffle, and rich="k", reshuffle="weights" correspond to weights & links reshuffle and rich="s", reshuffle="weights.local" correspond to directed reshuffle null model of your PRL 101 paper ?

Your comments on these will help me apply tnet to my data.

ciao,
jay

tnet cannot -so far- handle node labels, so they are simply the id numbers of the nodes.

The best way to enter larger networks is to load an edgelist file. You can do this by arranging your data in a txt file like this:

1 5 5
1 12 5
1 19 5
1 21 5
1 4 1
….

Then write in R: net <- read.table("filename.txt")

Hope this helps,

Tore