Great that you are considering the tie weights in the network.

The small_world_test_w-function was removed from tnet since many people had issues understanding exactly what it did. There is hopefully a paper coming out soon that details the steps of it, so then it might be re-introduced.

In the meantime, you can calculate the individual measures yourself as the underlying functions are still part of tnet. In particular, you can create a list-object with random networks, and then use the sapply-function with the distance_w to obtain benchmark values.

If you have any issues with the code, let me know.

Best,
Tore

I’m a social network novice. I am working with a weighted and directional network and I want to test the “small world effect” with tnet package. But the tnet showed an error message: Error: could not find function “small_world_test_w”. Could you help me?

Adam

An alpha closer to 1 would indeed do that. Nevertheless, the values of alpha still remain a bit of a question mark as simply the ranges are defined, but not exact values. I hope a study will come that assess optimal levels of alpha for various performance outcomes comes out soon (see the conclusion section of the paper for more details).

Tore

Thank you,
Liz

Thank you for using tnet.

You are absolutely correct on both your assumptions. All one-mode functions in tnet are applicable to directed networks, except when explicitly mentioned (i.e., the local clustering coefficient). Also, you might want to have a look at this post on Closeness centrality in networks with disconnected components as directed networks often do not have just a single strong component (all nodes can reach all other). This feature you can use with (1) the binary measure, alpha=0, (2) the first generation generalisation, alpha=1, and (3) the second generalisation with variable alpha.

Hope this helps,
Tore

Thank you so much for posting all of this information and for creating tnet! Being a social network novice, your papers and blog have been invaluable in my slog through the literature.

I do have two questions. First, I am working with a weighted and directional network and I want to calculate the closeness of each node. Am I right in thinking that the closeness_w function takes directionality into account? This looks to be the case based on the R documentation, but I thought I should check before I go ahead using the values.

Second, I read your 2010 paper in Social Networks and I wanted to clarify my understanding of alpha. As I understand it, if I want to take both the weight of the ties and the number ties for each node equally into account when calculating degree, closeness and betweenness, I should be using an alpha value between 0 and 1 (e.g. a=0.5). Is this correct? Sorry for the simple questions, but as I said, I’m very new to all of this and struggling to understand the basics.

Thank you very much in advance!
Liz

Thank you for finding tnet helpful. Some of my thoughts on projecting (binary and weighted) two-mode networks are available here.

Tore

Thank you,
Mike

tnet will only be able to handle longitudinal networks from version 3. These functions are still being tested.

Best,
Tore

Would appreciate your kind help.

Regards