How to make a plot that groups different type of nodes by cluster?

I have a dataframe df with nodes information like below:


structure(list(Names = c("A4GALT", "AASS", "ABCA10", "ABCA7", 
"ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2"), type = c("typeA", 
"typeA", "typeC", "typeA", "typeC", "typeC", "typeB", "typeB", 
"typeB"), type_num = c(1L, 1L, 3L, 1L, 3L, 3L, 2L, 2L, 2L), Clusters = c("Cluster1", 
"Cluster1", "Cluster2", "Cluster3", "Cluster3", "Cluster1", "Cluster2", 
"Cluster3", "Cluster2")), row.names = c(NA, 9L), class = "data.frame") 

So, in the df dataframe, there are 4 columns. Names is the gene name, type is different types A, B and C, type_num is the number given to each type and column Clusters show 3 clusters in which each gene belong to.

Similarly, I have other dataframe df2 with with edges information. And below I’m showing that:


structure(list(fromNode = c("A4GALT", "A4GALT", "A4GALT", "A4GALT", 
"A4GALT", "A4GALT", "A4GALT", "A4GALT", "AASS", "AASS", "AASS", 
"AASS", "AASS", "AASS", "AASS", "ABCA10", "ABCA10", "ABCA10", 
"ABCA10", "ABCA10", "ABCA10", "ABCA7", "ABCA7", "ABCA7", "ABCA7", 
"ABCA7", "ABCD4", "ABCD4", "ABCD4", "ABCD4", "ABHD4", "ABHD4", 
"ABHD4", "ABTB1", "ABTB1", "AC006978.2"), toNode = c("AASS", 
"ABCA10", "ABCA7", "ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2", 
"ABCA10", "ABCA7", "ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2", 
"ABCA7", "ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2", 
"ABCD4", "ABHD4", "ABTB1", "AC006978.2", "AC009119.2", "ABHD4", 
"ABTB1", "AC006978.2", "AC009119.2", "ABTB1", "AC006978.2", "AC009119.2", 
"AC006978.2", "AC009119.2", "AC009119.2"), weight = c(0.005842835, 
0.002253695, 0.014513253, 0.004851739, 0.066702792, 0.009418991, 
0.001136938, 0.000474221, 0.004405601, 0.000666001, 0.005625977, 
0.0333554, 0.004666223, 0.000103131, 0.00026302, 0.004514819, 
0.029632695, 0.001825839, 0.028379806, 0.001403298, 0.008339397, 
0.02393394, 0.004782329, 0.024767355, 0.002986813, 0.00559471, 
0.005961539, 0.064831874, 0.013023138, 0.027935729, 0.006618816, 
0.001134219, 0.012798368, 0.007961242, 0.01640476, 0.007997743
), direction = c("undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected", "undirected", "undirected", "undirected", 
"undirected", "undirected")), row.names = c(NA, -36L), class = "data.frame")

I wanted to create a network like below which represents the clusters with types having different colors in each cluster. It should look something like this:

In the above image the numbers represent different clusters and colors represent different types which belong to each cluster and connected.

Can anyone please help me how to create a network like above to collapse each cluster into a single network node and visualize a network with clusters as nodes. Any help is appreciated. thank you in advance.

To generate the network you can use graph_from_data_frame.

You can cluster the graph with several different algorithms, e.g. using cluster_infomap.

You can collapse each cluster in a single network node by using contract. You can simply provide the membership vector of the resulting clustering. You probably also want to simplify.

H <- contract(G, membership(communities))
H <- simplify(H, remove.loops=FALSE)

You can then simply plot the resulting graph H.

Thank you for your reply. What is the object G here. Which data did you take?

And I tried doing the network but it didn’t look good and I wanted to increase the distance between the clusters here [How to increase the distance between the clusters using igraph?]

This is the graph object, please see the documentation. I have not used any data, this is just an example. You should construct the graph G yourself by using graph_from_data_frame, as said.