[statnet_help] temporal ergm with partially observed networks
c.e.g.steglich at rug.nl
Tue May 12 23:29:19 PDT 2020
Dear Tom and Zack,
I'd like to also point to highly related work by Robert Krause on
missing data in ergm and stochastic actor-oriented network models (saom):
Krause, R. W., Huisman, M., & Snijders, T. A. (2018). Multiple
imputation for longitudinal network data. /Italian Journal of
Applied Statistics/, /30/(30), 33-58.
Krause, R. W., Huisman, M., Steglich, C., & Sniiders, T. A. (2018,
August). Missing network data: A comparison of different imputation
methods. In /2018 IEEE/ACM International Conference on Advances in
Social Networks Analysis and Mining (ASONAM)/ (pp. 159-163). IEEE.
Krause, R. W., Huisman, M., Steglich, C., & Snijders, T. (2020).
Missing data in cross-sectional networks–An extensive comparison of
missing data treatment methods. /Social Networks/, /62/, 99-112.
For the choice between discrete-time approaches (like tergm) and
continuous-time models (like saom), it can be helpful to consider
* whether time intervals are very small compared to the network
evolution speed (then both approaches may make sense but will not
differ much from each other, nor from normal, independence-assuming,
logistic regression*), and
* whether observation moments are clearly round-based (e.g., derived
from yearly publications) or clearly snapshots of a continuous-time
process (e.g., repeatedly measuring friendship in an organisation) -
see reference** below.
Like in the STERGM- approach, also in the saom-framework, the difference
between tie creation and tie dissolution is modelled by separate
functions (keyword: endowment, creation, evaluation functions - see
RSiena Manual***, p.13f).
All the best, Christian
* Lerner, J., Indlekofer, N., Nick, B., & Brandes, U. (2013).
Conditional independence in dynamic networks. /Journal of Mathematical
Psychology/, /57/(6), 275-283.
** Block, P., Koskinen, J., Hollway, J., Steglich, C., & Stadtfeld, C.
(2018). Change we can believe in: Comparing longitudinal network models
on consistency, interpretability and predictive power. /Social
Networks/, /52/, 180-191.
On 12/05/2020 21:19, 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!
> On Tue, May 12, 2020 at 1:26 PM Zack Almquist <zalmquist at uw.edu
> <mailto: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.
> 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
> <mailto: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,
> Thomas Kraft
> Postdoctoral Scholar
> Department of Anthropology
> University of California, Santa Barbara
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*Interuniversity Centre for Social Science Theory & Methodology*
Department of Sociology, Grote Rozenstraat 31, NL-9712 TG GRONINGEN
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