[statnet_help] temporal ergm with partially observed networks
zalmquist at uw.edu
Wed May 13 09:22:16 PDT 2020
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.
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):
Zack W. Almquist
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,
> 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:
>> Krivitsky, P. N., & Morris, M. (2017). Inference for social network
>> 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
>> > 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
>> > "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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>> Professor Emerita of Sociology and Statistics
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