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 -- graph-tool@skewed.de To unsubscribe send an email to graph-tool-leave@skewed.de