Inside the Voynich Network: graph analysis

[quimqu]

I also repeated the Voynich run for K = 5, this time restricting co-occurrence windows to stay within paragraph boundaries, so that tokens at the end of one paragraph don’t link to those at the beginning of the next (note that in the previous results, the window could cath words from the next paragraph; in this run, I avoided this). The difference is subtle but measurable across several KPIs.

Setting	Nodes	Edges	C	σ	Modularity	avg_degree	kcore_max	degree_gini	H_mean
Voynich (K=5)	8744	130797	0.674	201.41	0.237	29.91	85	0.621	3.68
Voynich (K=5, paragraph-limited)	8324	115394	0.712	219.29	0.228	27.73	82	0.651	4.28

Confining the windows within paragraphs slightly reduces the network size and density, but the structure becomes more tightly clustered (C rises from 0.67 to 0.71) and even more small-world (σ ≈ 219 vs. 201). Restricting co-occurrences within paragraphs increases clustering while keeping path lengths comparable, which numerically raises σ. In practice, this means the network becomes more locally cohesive rather than globally more efficient. The degree distribution flattens a bit (higher Gini), and modularity remains stable. Mean entropy increases (which is expected, as limiting context makes each local neighbourhood less diverse, reducing the smoothing effect of cross-paragraph transitions).

Limiting co-occurrences to within paragraphs makes the Voynich network smaller but more internally cohesive: words cluster more tightly inside paragraphs, paths stay short, and entropy rises as each section becomes more self-contained and less connected to the rest of the text.

The overall picture suggests that paragraph boundaries act as real topological separators in the Voynich text. When preserved, they sharpen the internal cohesion of each component while still keeping the global small-world pattern intact, reinforcing the impression of a text with strong local repetition and high global connectivity.

I am now re-running more text comparisons, even with language A and language B corpus (the Currier one's, not my topic modeling ones). I will try to post the results toorrow.