[statnet_help] Interactions in a distance-dependent model

Carter T. Butts buttsc at uci.edu
Sun Jul 19 04:15:20 PDT 2020

Hi, Adam -

If you have a mutuality term in the model, it already accounts for that
effect.  Do you intend to say that you expect proximate vertices to
reciprocate at higher rates - /above and beyond the propinquity effect/
- than distant vertices?  (Again, if you have a propinquity effect, it
will already be the case that, ceteris paribus, reciprocation will be
more likely for proximate vertices.)

If you want this type of effect, it can be realized with the dyadcov
term, or by creating an interaction term between mutual and edgecov.  I
don't think the user-level functionality for the latter option is yet
exposed, though it's pretty easy to code it using ergm.userterms;
however, dyadcov will probably suit your purposes.

Hope that helps,


On 7/19/20 3:07 AM, Adam Haber wrote:

> Hello,


> Following a recent question I’ve posted here (and got very helpful responses - thank you!), I’m trying to add more “domain knowledge” into the model. Specifically, the nodes in the network we're studying are embedded in 3D, and we’ve seen that (as expected) adding distance-dependence to the model (via an edgecov(x) term such that x is the distance matrix) indeed improves the GOF.


> I want to take this one step further: I know that if there’s a (directed) edge i->j, and j and k are “close” (spatially), it should increase the probability that there’s an edge i->k. Another option would be to “discretize” the distances and group nodes into groups of “spatial loci", and add a term that if there’s an edge i->j and j and k are in the same spatial cluster (a binary indicator function), than this should increase the probability of the edge i->k.


> Is there a way to incorporate this sort of reasoning into an ergm model using any of the available terms? I went over the examples I could find and did not encounter anything similar...


> Thanks again,

> Respectfully,

> Adam Haber


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