[statnet_help] fragmented bipartite network...

[Daniel Gotthardt]

Hello Harald,

if I understand you correctly you have a within-mode network as well as 
a bipartite network. James Hollway et al. (2017) has described an 
approach to handle these kinds of combined networks as multilevel social 
spaces with stochastic actor-oriented models: 
https://www.cambridge.org/core/journals/network-science/article/abs/multilevel-social-spaces-the-network-dynamics-of-organizational-fields/602BB810A44497EBDE2A111A6C2771A3 
- There are also some tricks to transform these types of networks into 
an extended multimodal network matrix, exemplified e.g. in Knoke et al. 
(2021): 
https://www.cambridge.org/core/books/abs/multimodal-political-networks/agency-influence-power/57CB185C6E9429B34A9DE181C37EADF3

I personally don't know of any ergm model that can handle this kind of 
co-evolution of one-mode and two-mode networks but some kind of 
multilevel ergms (see Wang et al. (2013) 
https://www.sciencedirect.com/science/article/abs/pii/S0378873313000051) 
might be the way to go: - I'm sure others here know more about the 
capabilities of ergm.multi though.

If these kinship structures explain the fragmentation of the bipartite 
network, you might need to include them either directly with the 
approaches above or construct some corresponding dyadic or monadic 
covariates to represent the kinship structure in your single level network.

Best Regards,

Daniel

Am 01.12.2023 um 02:13 schrieb Martina Morris:

> 

> Hi Harald,

> 

> I'm looking for some clarification here, which I think Tom Kraft might 

> also have wondered about.

> 

> You say:

>>

>> Our research focuses on tie formation and elite cohesion, specifically 

>> examining interlocking directorates and kinship relations. The 

>> dependent bipartite business network comprises 6,902 individuals and 

>> 5,178 companies, exhibiting sparsity (density = 0.00012) and 

>> fragmentation with 4,455 components, including 3,850 isolates in the 

>> first mode (persons)

>>

> For a bipartite network ties are allowed only between modes (persons, 

> companies), not within.  It's clear how interlocking directorates would 

> meet that criteria.  But kinship relations would be among persons, so 

> within-mode, not between, and this would not be a bipartite network.

> 

> Is the model you've sent us for the interlocking directorships only?  

> And by isolates in the person mode, do you mean persons who are not 

> affiliated with any of the companies?  If so, then it's a bit odd to 

> include them in the bipartite network.

> 

> I'm wondering if this problem is better posed as a multilevel network 

> (not my area of expertise).

> 

> thanks,

> Martina

> 

> 

> On Thu, Nov 30, 2023 at 4:33 PM Carter T. Butts <buttsc at uci.edu 

> <mailto:buttsc at uci.edu>> wrote:

> 

>     __

> 

>     Hi, Harald -

> 

>     Coexistence of large complex components does not generally occur

>     unless something drives the fragmentation, and this is what your

>     models are telling you: the terms you are currently using do not

>     include the forces that are sufficient to reproduce your component

>     size distribution.  That means that you need to think about why your

>     network is split into fragments, and include terms that capture the

>     relevant social forces.  Thinking about likely mechanisms is step

>     zero, so do that before anything else!  Guided by your substantive

>     knowledge of what is likely going on, you will next (as others have

>     said) want to look at covariate effects relating to differential

>     mixing, since those are your most obvious and most important sources

>     of heterogeneity.  If you find that there is still more

>     fragmentation that can be explained by other means, you may need to

>     consider model terms relating directly to component count or size. 

>     These are still somewhat experimental, and are currently sequestered

>     in an add-on package called ergm.components

>     (https://github.com/statnet/ergm.components

>     <https://urldefense.com/v3/__https://github.com/statnet/ergm.components__;!!K-Hz7m0Vt54!iKts-XLv39sY0gvmpW6MWLIxNMCNKjKQKOhJszIbp3PIy_J5mdLCs0MytfHsBu-cjnQjk997tCRX0MMs6LDW$>). However, this package can be installed from github (see the github page), and the terms will work automagically with ergm() and friends once the package is loaded.  Depending on your situation, you may need or want to examine the components() or compsizesum() terms, both of which are documented within the package.

> 

>     Hope that helps,

> 

>     -Carter

> 

>     On 11/30/23 9:58 AM, Harald Waxenecker wrote:

>>

>>     Dear ‘statnet community’,____

>>

>>     __ __

>>

>>     Our research focuses on tie formation and elite cohesion,

>>     specifically examining interlocking directorates and kinship

>>     relations. The dependent bipartite business network comprises

>>     6,902 individuals and 5,178 companies, exhibiting sparsity

>>     (density = 0.00012) and fragmentation with 4,455 components,

>>     including 3,850 isolates in the first mode (persons). The attached

>>     documents contain descriptives and the component size distribution

>>     from the observed network.____

>>

>>     ____

>>

>>     The fragmented structure is important, as other network layers,

>>     like kinship relations, are expected to contribute to the cohesion

>>     of this business network. We apply ERGM to model these processes,

>>     but we struggle to capture the fragmented structure of the

>>     observed network. The component size distribution of the simulated

>>     network differs significantly. In addition, the goodness-of-fit

>>     (GOF) for k-stars (in both modes) and geodesic distances (Inf)

>>     shows significant results. All these results are also attached.____

>>

>>     ____

>>

>>     We've explored various options, including constraints, MCMC

>>     propositions, and simulated annealing, but haven't achieved

>>     success. Please, we would like to ask for your help to improve our

>>     model. Thank you!____

>>

>>     __ __

>>

>>     Kind regards,____

>>

>>     Harald____

>>

>>     __ __

>>

>>     __ __

>>

>>     __ __

>>

>>     --- ____

>>

>>     *Harald Waxenecker

>>

>>     *____

>>

>>     *Masaryk University | Faculty of social studies*

>>     Department of Environment Studies

>>     A: Jostova 10 | 602 00 Brno | Czech Republic

>>     E: waxenecker at fss.muni.cz <mailto:waxenecker at fss.muni.cz>____

>>

>>     __ __

>>

>>

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-- 

Daniel Gotthardt, M.A.

Wissenschaftlicher Mitarbeiter / Research Associate

Universität Hamburg
Fakultät für Wirtschafts- und Sozialwissenschaften / Faculty of 
Business, Economics and Social Sciences
Fachbereich Sozialwissenschaften / Department of Social Sciences
Soziologie, insb. Digitale Sozialwissenschaft / Sociology, esp. Digital 
Social Science

Max-Brauer-Allee 60
22765 Hamburg
www.uni-hamburg.de