Hi Valerio,
Many thanks for getting back to me. I have re-read Tiago's
paper<https://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.042807> and
instead wonder whether the most appropriate null model for this example is
instead with random allocation of layers. As follows with this example:
g = gt.collection.ns["new_guinea_tribes"]
#layered model
state = gt.minimize_nested_blockmodel_dl(g,
state_args=dict(base_type=gt.LayeredBlockState,
state_args=dict(ec=g.ep.weight, layers=True)))
print(state.entropy())
##null with no layers
state_null = gt.minimize_nested_blockmodel_dl(g)
print(state_null.entropy())
##null but with random layers.
null_edge = g.new_edge_property("int")
rand = np.random.randint(len(np.unique(g.ep.weight.get_array())),
size=g.ep.weight.get_array().shape[0])
counter=0
for e in g.edges():
null_edge[e] = rand[counter]
counter+=1
state_null_v2= gt.minimize_nested_blockmodel_dl(g,
state_args=dict(base_type=gt.LayeredBlockState,
state_args=dict(ec=null_edge, layers=True)))
print(state_null_v2.entropy())
This example now gives me an entropy difference favouring the layered model
(state) compared to the null model with random layer allocation (state_null_v2).
Is that correct? Does that make sense?
BW
James
________________________________
From: RESTOCCHI Valerio <[email protected]>
Sent: 28 November 2021 11:12
To: Main discussion list for the graph-tool project <[email protected]>
Subject: [graph-tool] Re: Layered networks, compared to the a null model
Hi James,
I think that the main difference is that using the Layered Block State you do
retrieve a community structure, whereas in the other case you don’t.
Also, given the size of the network, I wouldn’t read much into it, but maybe if
you check the posterior distribution of partitions you will find that these are
two competing explanations of the data.
This is my interpretation (might be incorrect though).
Best,
Valerio
---------------------------------------------------------------------
Lecturer in Business Applications of Informatics
Room 2.18a - Informatics Forum
School of Informatics - University of Edinburgh
On 28 Nov 2021, at 10:44, James Ruffle
<[email protected]<mailto:[email protected]>> wrote:
This email was sent to you by someone outside the University.
You should only click on links or attachments if you are certain that the email
is genuine and the content is safe.
Dear Tiago/community,
I have been reading your 2015 article on layered networks, where you illustrate
favourable model fits on layered formulation compared to a non-layered null.
But, I was wondering if you could please clarify the appropriate null model
from which the comparisons in model entropy are then made?
Considering the following example, which is the example on the graph-tool
website:
g = gt.collection.ns["new_guinea_tribes”]
#layered model
state =
gt.minimize_nested_blockmodel_dl(g,state_args=dict(base_type=gt.LayeredBlockState,state_args=dict(ec=g.ep.weight,
layers=True)))
print(state.entropy())
##This gives me a model entropy of 166.92854839491955
#Presumably the null model is as follows, where one simply does not pass a
layered block state, and fits the model as if a singular edge layer?
state_null = gt.minimize_nested_blockmodel_dl(g)
print(state_null.entropy())
##But this gives me a model entropy of 113.74058711521675
I’m a little confused by this as, whilst the example on the website makes
perfect sense, then the model entropy seems to suggest I should favour the
non-layered null?
Thank you for your clarification.
BW
James
_______________________________________________
graph-tool mailing list -- [email protected]<mailto:[email protected]>
To unsubscribe send an email to
[email protected]<mailto:[email protected]>
The University of Edinburgh is a charitable body, registered in Scotland, with
registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh
Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336.
_______________________________________________
graph-tool mailing list -- [email protected]
To unsubscribe send an email to [email protected]
