> > > I am going to compare NestedBlockState.entropy() of the two run, > > > but I am not sure this is correct. > > > > > How should I take into account the fact that the networks are > > > slightly different? > > > > Would normalization make the two entropies comparable? I'd be > > interested to hear opinions about using, for normalization, the > > entropy of a NestedBlockState where each node is in its own group. > > The description length (DL) tells you how much information is needed > to encode both the network and the model parameters. If we compare > the DL for the same network but different models, this tells which > model most compresses the data. But if we compare two different > networks with two different models, this tells us very little, because it > mixes a comparison of which network is more regular with the quality > of fit of each model. > > The results of this kind of comparison is often trivial: the more nodes > and edges, the higher will be the DL. > > You *could* compute something like the DL per edge in order to > compare two networks, but since the DL is not a linear function of the > number of nodes or edges, it is difficult to put this evaluation on solid > statistical grounds.
Thanks Tiago, I see that this could be an option. But how about my proposal? The 'polbooks' dataset has 105 nodes. An SBM with one block (B=1) has a DL of about 1550 bits. The DL is minimized (DL_min=1300) for B=5. When each node is in its own block (D=105), DL is maximized (DL_max=1950). Can't I make states of different graphs comparable by taking DL_min/DL_max? It seems like a straightforward application of normalized entropy (https://en.wikipedia.org/wiki/Entropy_(information_theory)#Efficiency_(normalized_entropy)) to me. All, Tiago fixed a bug in the mailing list backend. It caused my email to arrive four times. I'm sorry for flooding your mailbox. Best wishes Haiko _______________________________________________ graph-tool mailing list -- graph-tool@skewed.de To unsubscribe send an email to graph-tool-le...@skewed.de
