[statnet_help] A couple of questions on modeling two-mode networks

[Steffen Triebel]

Greetings statnet-users!

As this is my first message on this list, a few words about myself to start with: I am a PhD student working on my final PhD paper in which I apply ERGMs. My primary research area is interorganizational networks. So far my dissertation has focused on network dynamics. As such, I have grown pretty confident in using SAOMs, but have had limited touching points with ERGMs. The other thing I’m unfortunately not that well-versed in (yet), but which I will analyze in this project, are two-mode networks – I don’t know why, but I just find them difficult to wrap my head around, so please forgive me if some of the questions in this mail are worded poorly or seem obvious to you.

 

This will be a rather long e-mail with many questions that have come up for me so far, so apologies up front and thanks to anyone answering some of the points below.

 

I have two general questions and then questions about specific effects:
My network specification is somewhat odd. On the first mode (actors), I have about 9.5k nodes. On the second mode (groups), I have about 500. Most of the actors are only connected to one group, while there are some who are connected to 2 groups and very few that are connected to 3+. This is not a data error – it’s just that these connections hold a lot of weight and that the actors basically connect the groups with each other and that these connections are meaningful. I am wondering if I should respect this in the model somehow by fixing certain effects (something like b1degree(1))? In a very basic specification, the model converges without issues and shows no degeneracy.
About data specification: I have an edgelist with actor attributes in the columns next to it. When I read this as a network, R rightfully turns the actors into unique nodes, omitting some rows. Is there an elegant way to keep the attributes while reading in the network or should I extract the unique actors after reading in the network and then match them with the respective attributes in an additional step?
I am unsure how to interpret the by-option in some effects. Would this just mean if I have a categorial variable (let’s say sex), I could set this to by=sex and will get different estimates based on each categorial value?
 

Now, a couple of questions to see if I understood certain effects right / understand how I should specify certain research interests:
I am very interested in single actors connecting multiple groups and I reckon this is portrayed by b1star(). Am I correct to assume that b1star(2) would mean likelihood of 2 groups being connected through 1 actor and that b1star(3) would mean the same for 3 groups respectively? And adding a covariate (again, let’s say sex) in the attribute-option would mean “likelihood of 3 groups being connected through 1 actor is increased for male actors”?
Let’s say I want to model that a female actor is more likely to be part of a certain group the more females are part of the group: Is b1nodematch(sex) correct?
Notwithstanding attributes or theory, I am structurally interested in two very basic things. First: General Popularity of groups (basically something like inPop in Siena models). My understanding is that b2sociality is picking out the role of single groups, so I assume it is b2degree. Is b2degree(1, levels=1) correct? Stupid addendum: How would I interpret a significant positive parameter here (just so I can get a good feel of wording and logic)?
Second: I want to understand how much more likely certain groups are in choosing actors who are already part of other groups. I am absolutely unsure how to specify this, especially when attributes come into play (such as: the higher attribute value X, the more likely the group is to recruit actors already active in other groups).
 

Thanks to anybody taking the time to read this far.

Steffen Triebel

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