Hi Jianjian,
The multilayer model as you've used works by fitting an independent SBM to
each layer, finding the best partition /across all layers/ to minimize the
sum of their description lengths.
To allow membership to vary across layers, you need to use the overlapping
model, then look at the
Hi there,
Layered overlapping models aren't working for GraphViews, due to the
following problematic lines in LayeredBlockState:
if overlap and self.ec is not None:
self.base_ec = self.base_g.own_property(ec.copy())
ec = agg_state.eindex.copy()
pmap(ec,
OK thanks for getting back to me! Looking forward to the update whenever it
may come.
Best,
John
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Apologies, gitlab won't let me sign in with any account at the moment for
some reason otherwise I would.
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Hi again,
On a similar note, can I quickly clarify that if I am comparing a binned
layer model for multigraphs to a suitable null model, the null model of eqn
(A3) corresponds to that of eqn (A2) with \ell in place of l, and then the
further terms displayed?
That is what would make sense to me
Hi Tiago,
Thanks for an amazing library! I'm trying to use the multilayer aspect of
graph-tool and I have a few questions that I haven't found answers to
online:
1) How do you access parameters inferred for independent models on separate
layers, say for reproducing Figure 5(b) from the paper?
Hi again,
I believe I've answered my first question (clumsily I suppose) by taking the
state inferred from the layered model, then taking e.g.
submodels=[]
lgs = state.get_levels()[0].gs
for l,lg in enumerate(lgs):
submodels.append(gt.NestedBlockState(lg,bs=[lvl.layer_states[l].b for
lvl in
Actually looking back is it instead that the null model in (A2) tells you
whether the full set of layers provides useful information, then in order to
compare between this and models with different bins you need to incorporate
the additional terms of (A3) (rather than solely the output
