[statnet_help] temporal ergm with partially observed networks

Tom Kraft kraft.tom at gmail.com
Tue May 12 12:19:11 PDT 2020

Hi Zack,

Thank you for this helpful reference. I had come across this paper in my
literature search but should have given it more attention.

Is there a place where the source code associated with your paper is
available? It remains a bit unclear to me how several of the strategies you
used (Shat approximation) might be implemented in R.

I suppose an alternative is to use multiple imputation as discussed here:
Wang, C., Butts, C. T., Hipp, J. R., Jose, R., & Lakon, C. M. (2016).
Multiple imputation for missing edge data: a predictive evaluation method
with application to add health. *Social networks*, *45*, 89-98.

Thank you!

On Tue, May 12, 2020 at 1:26 PM Zack Almquist <zalmquist at uw.edu> wrote:

> Hi Thomas,


> My paper on missing data for TERGM style analysis might be helpful; take a

> look at it and see if it is addressing your problem of interest:


> Almquist, Zack W., and Carter T. Butts. "Dynamic network analysis with

> missing data: theory and methods." *Statistica Sinica* 28.3 (2018):

> 1245-1264.


> Best,


> Zack

> ---

> Zack W. Almquist

> Assistant Professor

> Department of Sociology

> Senior Data Scientist Fellow, eScience Institute

> University of Washington



> On Tue, May 12, 2020 at 8:25 AM Tom Kraft <kraft.tom at gmail.com> wrote:


>> Dear statnet,


>> Thank you for all your wonderful work. I am looking for advice on methods

>> applicable to network data that doesn't fit neatly into the types of data

>> that regularly appear in statnet tutorials. I have a list of sequential

>> discrete time networks involving the same set of actors. At each timepoint,

>> however, I only observe some percent of actors in the whole network (~30%).

>> For all actors observed I have complete knowledge of their ties, including

>> the identity of alters. I would like construct a model of these empirical

>> data with relatively basic terms that can be used to simulate full temporal

>> networks with similar network structures.


>> Given the temporal nature of the networks, it seems that a standard tergm

>> could appropriately be used to model the formation and dissolution of ties.

>> Yet given the nature of the sampling, for any given network I'm inclined to

>> think that the data are essentially egocentric with known alter info

>> because if I have information on a node I can be sure that I know all the

>> connections of that individual. Thus, I am wondering if it is possible to

>> conduct a temporal version of the ergm.ego model. From the materials I have

>> found online, it seems this should be possible:


>> "The principles of egocentric inference can be extended to temporal

>> ERGMs (TERGMs). While we will not cover that in this workshop, an example

>> can be found in another paper that sought to evaluate the network hypothsis

>> for racial disparities in HIV in the US (Morris et al. 2009)." -

>> http://statnet.org/Workshops/ergm.ego_tutorial.html#6_example_analysis


>> However, I am unable to find worked examples to follow up on that

>> reference/approach and it is not clear to me how to implement this.

>> Additionally, it seems like ergm.ego is not designed to incorporate

>> information on identifiable alters -- in which case the methods developed

>> in Kosikinen and Robins (2010) perhaps would be more appropriate so that

>> this useful information is not ignored.


>> Alternatively, I could imagine that this analysis is best conceived of as

>> a tergm with missing data at each time step. In this case info on alter ids

>> could be fully utilized I think.


>> I would be very grateful if anyone could comment on whether one of these

>> approaches seems feasible, or if there are other options I might consider.

>> Any materials or references to vignettes/tutorials/papers on the topic

>> would also be appreciated. Thank you in advance! Best,


>> Tom


>> Thomas Kraft

>> Postdoctoral Scholar

>> Department of Anthropology

>> University of California, Santa Barbara

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