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? Unless you're just masking the graph and only drawing edges applicable to each layer?
2) I've seen you say several times that it's simple to incorporate binning of layers into the model, but I haven't been able to figure out how to do so - a brief example of how to do so would be hugely appreciated! (or some initial pointers if you're pressed for time)
3) In this binning procedure, is it possible to keep subsets of layers separate? Each of my layers is actually defined by a (property X, property Y) combo, and I would like to investigate how property X affects the grouping of property Y, as well as whether it noticeably impacts the networks structure.
Apologies if I've missed the obvious! I've been trying to familiarise myself quickly with the package, and have hugely appreciated that you've coded this all yourself in the first place!
Thanks, John
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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 state.get_levels()]
but presumably there's a cleaner way of accessing these values.
For my second question, when you say in the paper to apply agglomerative hierarchical clustering, is there a cleaner way of doing this through graph-tool than just brute force? I.e. fitting the model for different bins then comparing description lengths (including subtracting log eqn 18 or 19) for each possible combination? This seems like it would be quite slow for non-small networks, especially for a reasonable initial number of layers, so again I assume there's an alternative.
If not then my third question is answered, as I can just suitably modify the prior then only trial merges for property Y.
Thanks, John
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Am 18.08.20 um 01:41 schrieb fitzgeraldj:
- How do you access parameters inferred for independent models on separate
layers, say for reproducing Figure 5(b) from the paper? Unless you're just masking the graph and only drawing edges applicable to each layer?
I don't understand the difference between these two things; they seem the same to me.
But in short: the second option (masking) seems the most straightforward.
(The LayeredBlockState object keeps this information in a manner that is most efficient for the inference algorithm, but not for visualization.)
- I've seen you say several times that it's simple to incorporate binning
of layers into the model, but I haven't been able to figure out how to do so
- a brief example of how to do so would be hugely appreciated! (or some
initial pointers if you're pressed for time)
I assume you mean automatic binning; unfortunately this is not yet implemented in the library. It's in my TODO list, but you know how that goes.
Best, Tiago
OK thanks for getting back to me! Looking forward to the update whenever it may come.
Best, John
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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 (i.e. account for distribution of edges first to binned layers, then between binned layers and sublayers), but then would clearly make the additional binned layer terms cancel, reducing (A3) back to (A2) in terms of the extra factors (but of course with different priors over parameters, and the binning prior also to take into account). Is that correct?
Thanks, John
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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 likelihood)?
Thanks, John
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fitzgeraldj -
Tiago de Paula Peixoto