Use of edge's weights in model inference

graph-tool
1

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

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2

Are the edge weights integers or real?

3

The edge's weights are integers

2016-11-15 18:17 GMT+01:00 Andrea Briega <annbrial(a)gmail.com>:

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4

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

5

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(a)gmail.com>:

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6

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

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