[statnet_help] temporal ergm with partially observed networks

Christian Steglich 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.

*** http://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf


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!

> Tom

>

> 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.

>

> 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

> <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)." -

> 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

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