On 15.11.2016 19:24, Andrea Briega wrote:

The edge's weights are integers

In this case, you should just pass the property map with the weights as the "eweight" parameter, e.g.

state = minimize_nested_blockmodel_dl(g,state_args=dict(eweight=weights))

Best, Tiago

2016-11-15 18:17 GMT+01:00 Andrea Briega <annbrial@gmail.com mailto:annbrial@gmail.com>:

Dear Dr. Tiago Peixoto, 

I have a question regarding weighted graphs. I would like to classify a
network with weighted edges into groups taking into account these
weights. I have used minimize_blockmodel_dl and
minimize_nested_blockmodel_dl before but I don't know exactly how to
implement these functions in a weighted network. I'm not sure if I have
to use the layered version, but in this case I don't know how to get
these layers from my network.

Thank you very much and sorry for the basic question,

Andrea

---

graph-tool mailing list graph-tool@skewed.de https://lists.skewed.de/mailman/listinfo/graph-tool

How do you fit a (nested) blockmodel if the edge weights are real?

On Wed, 16 Nov 2016 at 06:55 Andrea Briega annbrial@gmail.com wrote:

I hadn't found that option, it's much easier than I thought. Thank you very much!

2016-11-15 19:24 GMT+01:00 Andrea Briega annbrial@gmail.com:

The edge's weights are integers

2016-11-15 18:17 GMT+01:00 Andrea Briega annbrial@gmail.com:

Dear Dr. Tiago Peixoto,

I have a question regarding weighted graphs. I would like to classify a network with weighted edges into groups taking into account these weights. I have used minimize_blockmodel_dl and minimize_nested_blockmodel_dl before but I don't know exactly how to implement these functions in a weighted network. I'm not sure if I have to use the layered version, but in this case I don't know how to get these layers from my network.

Thank you very much and sorry for the basic question,

Andrea

---

graph-tool mailing list graph-tool@skewed.de https://lists.skewed.de/mailman/listinfo/graph-tool