I am attempting to use ERGM to analyze the formation mechanisms of non-local collaboration networks. In my network, intra-regional collaborations are not allowed due to my prior setup. If intra-group edges are not restricted in the ERGM analysis, it results in poor simulation performance.

My objective is to add a constraint to the model that “only allows edges between nodes across different groups, while prohibiting edges within the same group.” However, after reviewing the ERGM manual and posts discussing objectives opposite to mine, I am still at a loss, hence seeking help from the community.

Due to the large size of my network, approximately 20,000 nodes, I apologize for not providing the data and code. The sample code I am using is from this post.

# load necessary packages
library(ergm)
library(ergm.count)

# create a toy example network to illustrate the issue presented  
VEC=c(0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,4,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,6,2,4,4,2,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,5,0,1,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0)
mat_ex=matrix(VEC,25,25)

nb=1:25
ID=rep("ID_",25)
names=paste0(ID,nb)
rownames(mat_ex)=names
colnames(mat_ex)=names

SeAg=c("F_Adult","F_Adult","M_Adult","M_Adult","M_Sub-Adult","F_Adult","M_Adult","F_Adult","M_Sub-Adult","M_Adult","F_Adult","M_Adult","M_Adult","M_Adult","F_Adult","F_Adult","F_Adult","F_Adult","F_Adult","M_Sub-Adult","M_Adult","M_Adult","M_Adult","F_Adult","M_Adult")
Group=c(1,1,1 ,1,1 ,2, 2, 2 ,2, 2, 2, 2, 3, 3, 3, 3, 3, 4 ,4 ,4, 4, 4, 4,4, 4)
network_ex=as.network(x = mat_ex,
                            directed = TRUE,
                            loops = FALSE,
                            matrix.type = "adjacency",
                            ignore.eval=FALSE,
                            names.eval='weight')
set.vertex.attribute(network_ex, "Sex_Age", as.character(SeAg))
set.vertex.attribute(network_ex, "Group", as.character(Group))

# run the ergm function with blockdiagonal constraint
ergm_ex <- ergm(network_ex~ 
                  sum
                +nodematch("Sex_Age",diff=TRUE,levels=c(1,2),form="sum")
                , response = 'weight'
                 , constraints = ~blockdiag("Group")
                , estimate = 'MLE'
                , reference = ~Poisson
                , control = control.ergm(MCMC.interval = 1000
                                         , MCMLE.maxit = 200
                                         , init.method = 'CD'
                                         , MCMC.samplesize = 1000
                                         , MCMC.prop.weights="random"
                                         , MCMC.burnin=100,seed=12345)
)
summary(ergm_ex)