I have 20 igraph directed networks called pathway_1, pathway_2, pathway_3,..., pathway_x and a small GRN with 100 interactions, i.e:
AKT -> MYC
POU5F1 -> JUN
EGR1 -| AKT
JUN -| POU5F1
MYC -| EGR1
Where " -> " means activation and " -| " deactivation.
Since I have 20 different networks looking like the example above, I was wondering if there is a way to "convert" those pathway_x into communities as a whole, in order to search the GRN info into each one of those paths and then plot the results as heatmap or highlight the GRN inside of a large igraph containing all those 20 pathway_x.
I tried with cluster_fast_greedy but it only works with undirected graphs. I tired with cluster_spinglass but somehow I lose important info in my pathway_x.
Not sure if it's exactly what you want, but you could identify community structures in each graph and then compare each community via the variance of information (vi) metric.
In my tutorial posted on Biostars (here: Network plot from expression data in R using igraph ) I identify communities with
edge.betweenness.community
.