Hello Everyone.
I am (almost) new to R and need help to perform a calculation with the cluster_infomap function
I need to run this function in a bipartite network with weights. I have tried Infomap and have one of the examples after this question formatted to use infomap in its online version
https://www.mapequation.org/
In this format the bipartite network is defined with the number of vertices or nodes of both types, in this case 36 following with the nodes and their label, ending with the line *Bipartite 10 , which indicates that from node 10 the nodes are of the second type (in my case countries) and from this line three items:A node of type A, a node of type B and the weight of its link in percentage (values from 0 to 1)
I need to pass the same data to R in a graph and a vector with the weights of the axes as indicated in https://igraph.org/r/doc/cluster_infomap.html
I would very much appreciate the help of the functions needed to be able to run infomap and other community detection functions like Louvain to this example and other data.
Thank you very much in advance
Kind regards
------begin of dataset--------
*Vertices 36
1 "A Coruña y Ferrol - San Cibrao"
2 "Avilés y Gijón"
3 "Barcelona"
4 "Bilbo/Bilbao"
5 "Cartagena"
6 "Castelló/Castellón"
7 "Huelva"
8 "Marín-Pontevedra, Vigo y Vilagarcía"
9 "Tarragona"
10 "Albania"
11 "Bahréin"
12 "Bélgica"
13 "Canadá"
14 "Colombia"
15 "Croacia"
16 "Cuba"
17 "Dinamarca"
18 "Egipto"
19 "Estados Unidos"
20 "Francia"
21 "Irlanda"
22 "Italia"
23 "Jordania"
24 "Líbano"
25 "Madagascar"
26 "Malta"
27 "Marruecos"
28 "Montenegro"
29 "Noruega"
30 "Países Bajos"
31 "Portugal"
32 "Reino Unido"
33 "Suecia"
34 "Túnez"
35 "Turquía"
36 "Ucrania"
*Bipartite 10
1 18 0.04
1 27 0.02
1 34 0.03
2 12 0.03
2 14 0.11
2 15 0.04
2 20 0.07
2 27 1.00
2 29 0.04
2 32 0.10
2 34 0.03
2 35 0.03
2 36 0.13
3 20 0.05
3 21 0.01
3 22 0.10
4 12 0.05
4 16 0.02
4 17 0.05
4 20 0.10
4 21 0.03
4 22 0.10
4 24 0.11
4 26 0.03
4 27 0.11
4 28 0.03
4 30 0.02
4 31 0.07
4 33 0.02
4 34 0.11
4 35 0.04
5 10 0.03
5 18 0.49
5 22 0.09
5 23 0.08
5 24 0.03
5 25 0.03
5 27 0.10
5 28 0.05
5 34 0.38
5 35 0.26
6 11 0.17
6 13 0.17
6 18 0.21
6 19 0.02
7 18 0.03
7 27 0.06
7 34 0.03
8 27 0.02
8 34 0.03
9 12 0.01
9 22 0.40