[statnet_help] temporal ergm with partially observed networks

martina morris morrism at uw.edu
Wed May 13 07:43:00 PDT 2020

Hi Tom,

The alternative approach may be to treat your observed sample
egocentrically, and use our egocentric inference for ergms/stergms.

If the terms you anticipate using are restricted to those that can be
estimated from egocentric data (basically, the dyad-independence terms
plus degree distributions) these methods would allow you to estimate
stergms, and simulate networks that reproduce the sufficient statistics in
expectation. This is what we are using for our epidemic modeling studies.

Two refs of interest:

Krivitsky, P. N., & Morris, M. (2017). Inference for social network models
from egocentrically sampled data, with application to understanding
persistent racial disparities in hiv prevalence in the us. Annals of
Applied Statistics, 11(1), 427-455. doi:10.1214/16-aoas1010

Tutorial for our software:


On Tue, 12 May 2020, Tom Kraft wrote:

> 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!

> Tom


> 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)."



> 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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