I’m glad you’re finding the site useful.

1) To calculated node centrality on a projected two-mode network, you must use the one-mode metrics. See http://toreopsahl.com/tnet/weighted-networks/node-centrality/

2) Two-mode networks (tnet is limited to two-mode networks for multi-mode networks) are generally undirected, or directional in one way only. To the best of my knowledge, there are no general directional two-mode metrics.

Good luck,

Tore

Congrats for your website, I find it very helpful and thanks for sharing your knowledge.

I have two questions, and I would be grateful if you could answer : Can I calculate centrality measures on the projected network?

Also, is it possible, in a multi-mode network for the one set of actors to be directed(and if yes, can I use your package to turn it into a projected network)?

Thanks,

Andy

Thanks for your kind information. Now I got the answer.

Kind regards,

]]>You are simply using the projection_tm-function with method=”Newman” and the network being a weighted two-mode network: see line #8 in the fourth and final code block above.

Tore

]]>Regards,

]]>Hope this helps,

Tore

Hi,

I’m working on two-mode networks. In above post the tnet command for extended weighted network formula is not given.

Only binar,sum and newman is given. Kindly if possible can you please tell me the command for last method.

I’ll remain thankful.

Kind regard,

A.W.Mahesar

]]>Great to see more open science being done! I’m not sure there is a specific theory I can point you too regarding projects as there is so many. For example, two-mode networks with people and events can be projected due to assumed temporal geographical co-location. However, I believe it really depends on your context which theories of connectedness would apply.

Best,

Tore

Great suggestion. It does sound like a good idea as differences in tie weights would discount the tie strength. I haven’t implemented this type of projection method for two-mode networks; however, if you connect with me by email, this code can easily be created.

Best,

Tore

Great blog, this section in particular is my go-to guide to two-mode networks.

I am interesting in using the geometric mean, instead of the sum, to calculate the strength of an interaction between 2 nodes when projecting a 2-mode network onto a one-mode network.

The strength of the interaction between nodes A and B in the above example (leaving out Newman’s correction for now) would be 2 in this case (or 4.24 if you sum the weights directed at nodes they share before taking the geometric mean).

Do you think this is sensible, and can it be done in tnet?

Cheers

David

]]>Glad you found it useful. I am not entirely sure what you are referring to when you mention neighborhood measures. However, if you would like to calculate the number of common nodes between primary nodes in a two-mode network, you can use the projecting_tm(net, method=”sum”). This function will produce pairs of primary nodes (columns 1 and 2) with a third column with the number of common nodes.

Best,

Tore

Thank you for your article. I was looking for something which would be illustrative and precise. And your article is just that. I am currently working on a two mode bipartite network. Do you know how I can use neighborhood measures like Common Neighbors in the bipartite setting? Thanks ]]>

Best

Snehal ]]>

Thanks! I have not directly worked with 3-mode networks, so I haven’t given too much thought. I think this is an exciting area of new research with many unanswered questions. Do you project from 3-mode to two-mode by connecting primary and secondary nodes connected to the same tertiary nodes? What do you do with existing connections between primary and secondary nodes?

Best,

Tore

Great resource particularly for weighted 2-mode networks.

I am working with 3-mode networks and figured that I’ll have to convert (project) it to 2 modes to get some measures. Can you suggest any literature that deals with this conversion? Right now I am just replicating the 2->1 mode procedures. Your comments/suggestions are greatly appreciated.

Thanks!

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