[statnet_help] ergm inquiry

[James Moody]

Never underestimate the power and usefulness of basic descrptives and visualizations. The insight gained by seeing the clustering and heterogeneity is key; then model. Most of the time, it will confirm what you learned … but starting with modems sans deep investment in learning the case is a recipe for frustration. 

Sent from my iPad


> On Aug 13, 2023, at 6:27 PM, Julia Vassey <vassey at usc.edu> wrote:

> 

> Thank you!!

> 

>> On Sun, Aug 13, 2023 at 6:21 PM Carter T. Butts <buttsc at uci.edu> wrote:

>> 

>> Hi, Julia -

>> 

>> Looking at your figure, you'll definitely want mixing terms (nodematch, nodemix), because your clustering is very strongly attribute-based.  But I also see that you have a situation where not all nodes are participating in those subgroup interactions, so one key to getting this model to work is likely to be capturing the combination of the homophily/differential mixing among the nodes that do interact, and heterogeneity in propensity to interact at all. nodecov/nodefactor and degree terms can be useful for the latter, and you may indeed need an isolates term.  In any event, the figure you sent already provides a lot of helpful clues for modeling, so keep up with the visualizations and you are likely to find your way!

>> 

>> Hope that helps,

>> 

>> -Carter

>> 

>> On 8/13/23 1:56 PM, Julia Vassey wrote:

>> 

>> Thank you Michal and Carter! Appreciate all the suggestions. I do have

>> cohesive groups, mostly by geographic regions and they are represented

>> by color in this attached plot. I have not yet tried nodeifactor or

>> nodeofactor, but I will.

>> 

>> On Sun, Aug 13, 2023 at 9:43 AM Michał Bojanowski <michal2992 at gmail.com> wrote:

>> 

>> Hi Julia,

>> 

>> Thanks. Some questions and hypotheses about model specification:

>> 

>> 1. The network is directed. Are you sure you want nodefactor (nodes in

>> some groups have more ties than nodes in other groups irrespectively

>> of tie direction) rather than nodeofactor (same but for outgoing ties

>> only) or nodeifactor (same but for incoming ties only)?

>> 

>> 2. Your model does not have any nodematch terms on the nodal

>> attributes. Is that intentional?

>> 

>> 3. The ESP distribution is notably relatively flat. One mechanism that

>> might be responsible for that is strong community structure or

>> homophily on some observed attribute -- triangles get closed within

>> groups only and the count of ESP depends on group size. Do you have

>> several cohesive groups of varying size in the network?

>> 

>> m.

>> 

>> On Fri, Aug 11, 2023 at 4:51 PM Julia Vassey <vassey at usc.edu> wrote:

>> 

>> Thank you Michal! Good to hear from you.

>> 

>> Please, see the plots, attached. I have tried changing the decay

>> parameter multiple times, and the model fit gets better when

>> increasing the parameter: from 0 to 0.3 (gwesp(0.3, fixed = TRUE)),

>> but beyond 0.3 the model starts having issues with convergence.

>> p values for all esps are always very low.

>> 

>> Thank you for helping!

>> 

>> Julia

>> 

>> 

>> On Fri, Aug 11, 2023 at 8:53 AM Michał Bojanowski <michal2992 at gmail.com> wrote:

>> 

>> Hi Julia,

>> 

>> Can you send the GOF plots attached? In what way does the ESP not fit

>> well? Perhaps it is a matter of changing the decay parameter?

>> 

>> Michal

>> 

>> On Wed, Aug 9, 2023 at 1:00 PM Julia Vassey <vassey at usc.edu> wrote:

>> 

>> Dear All,

>> 

>> This is my first time posting a question in statnet help. If the question needs to be posted/sent to a different email please let me know.

>> 

>> I have a question related to ergm. I am running an ergm model on a directed unipartite network of ~100 nodes (certain social media users) and ~700 edges. The model includes geolocation attributes of the nodes (geographic regions) and themes the nodes post about on social media.  The model also includes terms (mutual, gwesp) provided in the code below.

>> 

>> I am struggling with achieving a decent goodness of fit for edgewise shared partners using gof function. i/o degree, geodesic distance and model statistics look much better. I tried different things to try to improve edgewise shared partners, but nothing seems to work. The configuration below provides the best fit, however, I want to keep trying to improve the fit for edgewise shared partners. I appreciate any thoughts and comments on how to achieve this.

>> 

>> model = ergm::ergm(netg_infl ~ edges + nodefactor('region_code', levels = -4) + nodematch('region_code', diff = T, levels = -3) + nodefactor('topic_sum_binary', levels = -LARGEST) + nodefactor('marijuana_recode', levels = -c(1,3)) + nodefactor('nature_recode', levels = -c(1,3)) + nodefactor('health_life_recode', levels = -c(1,3)) + nodefactor('gaming_recode', levels = -c(1,3)) + nodefactor('food_recode', levels = -c(1,3)) + nodefactor('clothing_recode', levels = -c(1,3)) + mutual  + gwesp(0.28, fixed = TRUE) + offset(isolates), offset.coef = -Inf, control=control.ergm(parallel=2, parallel.type="PSOCK"))

>> 

>> Thank you,

>> 

>> 

>> --

>> Julia Vassey

>> Health Behavior Research

>> Department of Population and Public Health Sciences

>> Keck School of Medicine

>> University of Southern California

>> 

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