Am 26.02.21 um 20:00 schrieb ulgenklc:
I have a serious problem with getting the node labels. The code below only returns the node membership information for N nodes(size of the aggregated network). But since this is an evolving network, nodes are expected to change communities over time, so below code should return NxT(number of nodes times number of layers) many community labels?levels = state.get_levels() for s in levels: print(s) This returns the network partition at different levels of the nested SBM but only for N many nodes. What am I missing?
You are missing the fact that the layered model only has a single partition shared between all the layers.
If you want a model that gives a different partition for every layer you can either:
1. Split each layer into a separate graph, and fit a different SBM for each layer.
2. Fit an overlapping SBM (i.e. by passing overlap=True to LayeredBLockState), which will cluster the "half-edges", and hence give partitions that (potentially) vary between the layers.
Best, Tiago -- Tiago de Paula Peixoto <[email protected]> _______________________________________________ graph-tool mailing list [email protected] https://lists.skewed.de/mailman/listinfo/graph-tool
