[statnet_help] temporal ergm with partially observed networks

Zack Almquist zalmquist at uw.edu
Wed May 13 09:22:16 PDT 2020


Hi Tom,

I really like Martina's proposal as I think it makes a lot of sense given
the description of your data!

There are obviously a lot of dynamic network models and ways for
handling missing data -- I trust you to find the best one for your problem!
Christian has provided a nice set of papers for the SAOM framework that has
a rich history in dynamic network modeling (as he pointed out); and there
are also the relational event modeling frameworks (if you are going to work
in continuous time, e.g., Butts, Carter T. "4. A Relational Event Framework
for Social Action." *Sociological Methodology* 38.1 (2008): 155-200). Note
I am not trying to be comprehensive!

As per my method, I have a paper in JCGS with some software:

Mallik, A., & Almquist, Z. W. (2019). Stable Multiple Time Step
Simulation/Prediction From Lagged Dynamic Network Regression Models. *Journal
of Computational and Graphical Statistics*, *28*(4), 967-979.
https://cran.r-project.org/web/packages/dnr/index.html

That could be readily adapted for the methods in

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 Wed, May 13, 2020 at 8:47 AM Tom Kraft <kraft.tom at gmail.com> wrote:


> 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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