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--></style></head><body lang=DE link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal><span lang=EN-US>Greetings statnet-users!<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>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.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>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.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>I have two general questions and then questions about specific effects:<o:p></o:p></span></p><ul style='margin-top:0cm' type=disc><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>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.<o:p></o:p></span></li><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>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?<o:p></o:p></span></li><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>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?<o:p></o:p></span></li></ul><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Now, a couple of questions to see if I understood certain effects right / understand how I should specify certain research interests:<o:p></o:p></span></p><ul style='margin-top:0cm' type=disc><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>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”?<o:p></o:p></span></li><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>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?<o:p></o:p></span></li><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>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)?<o:p></o:p></span></li><li class=MsoListParagraph style='margin-left:0cm;mso-list:l0 level1 lfo1'><span lang=EN-US>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).<o:p></o:p></span></li></ul><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Thanks to anybody taking the time to read this far.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Steffen Triebel<o:p></o:p></span></p></div></body></html>