[statnet_help] temporal ergm with partially observed networks

Tom Kraft kraft.tom at gmail.com
Wed May 13 08:45:21 PDT 2020


Hi Martina,

Thank you for the input! The case I am describing here is in fact part of
an epidemic modeling study. The main problem I see with the approach of
treating the network egocentrically is that I will be forced to sacrifice
information about identifiable alters (again, if a node is observed then I
know with certainty all of the other actors they are linked to and their
identities, and I also know the full list of people they are not connected
to). As such, like you say I won't be able to model dependency terms like
gwesp. But these are undirected contact networks in which if A-B and B-C,
then A-C necessarily exists as well.

To be sure that I am understanding your suggestion: For a given timestep,
completely remove all unobserved nodes from the network. Estimate
parameters using an ego ERGM. Then simulate from the model onto a full
network containing the observed and unobserved nodes and information about
their nodal and edge covariates.

Is this correct? I am familiar with the tutorial link you sent, but it
unfortunately does not cover an egocentric application of a stergm. Do you
know of any worked examples like that which are available?

Thank you again,
Tom

On Wed, May 13, 2020 at 9:43 AM martina morris <morrism at uw.edu> wrote:


> 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:

>

> Theory:

> 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:

> https://statnet.github.io/Workshops/ergm.ego_tutorial.html

>

>

>

>

> hth,

> Martina

>

>

> 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)."

> > - 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

> > _______________________________________________

> > statnet_help mailing list

> > statnet_help at u.washington.edu

> > http://mailman13.u.washington.edu/mailman/listinfo/statnet_help

> >

> >

> >

>

> ****************************************************************

> Professor Emerita of Sociology and Statistics

> Box 354322

> University of Washington

> Seattle, WA 98195-4322

>

> Office: (206) 685-3402

> Dept Office: (206) 543-5882, 543-7237

> Fax: (206) 685-7419

>

> morrism at u.washington.edu

> http://faculty.washington.edu/morrism/

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