Ben - 1 year ago 202

Python Question

I detected communities in a large network and got a low modularity in igraph Python. I suspected the result. So I tried again in igraph R. The modularity was much higher. I do not know the reason. In the following, I will write a sample network and the code I used.

graph:(ncol format. The graph is undirected. The third column is weight.)

`1 2 123`

1 3 32

2 3 523

3 6 3

6 5 11

6 8 234

5 8 324

3 9 234

9 11 32

9 12 5534

9 13 32

11 12 322

11 13 3

12 13 32

R code:

`library(igraph)`

g=read.graph('graph.ncol',format='ncol',directed=F)

c=multilevel.community(g)

modularity(c)

[1] 0.2845496

Python code:

`import igraph`

g=igraph.Graph.Read_Ncol('graph.ncol',directed=False)

c=g.community_multilevel()

c.modularity

0.4974489795918367

In my original network, the number of communities using R and Python are much different. It is not only the multilevel method. I also tried fastgreedy method. The results using R and Python are also different.

Answer Source

The R interface is automatically using the weights from the `weight`

attribute when it calculates the community structure and the modularity, while the Python interface doesn't. E.g., in Python:

```
>>> g=load("test.ncol", directed=False)
>>> g.multilevel_community().q
0.4974489795918367
>>> g.multilevel_community(weights=g.es["weight"]).q
0.2845496103894415
```