Dear Snehal,

I am not sure what others would say, but, in your case, I am pretty sure
that you have used the overlapping version of SBM. You will not observe the
core-periphery structure fitted by the vanilla SBM, nor the assortative
structure via the degree-corrected SBM.

Try:
state = gt.minimize_blockmodel_dl(G, deg_corr=True, overlap=False, B_min=2)
And visualize it via:
state.draw(pos=G.vp["pos"], vertex_shape=state.get_blocks())

You may get what you expected.

Also, note that if you tried (without the minimum block number constraint),
state = gt.minimize_blockmodel_dl(G, deg_corr=True, overlap=False)

You will still obtain a ground state with only one non-empty group. This is
valid since it is a state with the minimum description length (MDL) of both
the model and the data.
See this paper for details about MDL:
http://dx.doi.org/10.1103/PhysRevLett.110.148701

Note that people have argued whether it is necessary to introduce the
additional Order(N) parameters in the SBM to fit such a small karate club
data, which introduces a lot of model complexity. Indeed, if one used a full
Bayesian treatment and study the relation of the model likelihood to the
different numbers of blocks used. For the karate network, one may hesitate
which number of partitions to use.
See the Fig.2(a) of this paper:
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.117.078301

Finally, the karate network is not generated by any model of the SBM-family.
So it really takes interpretations between the model and the data!


Best regards,
Tzu-Chi Yen



--
View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/Inference-for-the-karate-network-tp4027197p4027198.html
Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.
_______________________________________________
graph-tool mailing list
graph-tool@skewed.de
https://lists.skewed.de/mailman/listinfo/graph-tool