Inside the Voynich Network: graph analysis

[quimqu]

Quimqu, if it is possible, could you comment the results of Rohonc Codex? That would be particularly interesting for me.

It seems quite extreme on some dimensions but I'm not sure how to interpret these results.

I asume that each number separated by a space is a "word2. With the results, the Rohon text behaves very differently from real writing. When you study how its words appear next to each other, the pattern looks too regular. In normal languages, words mix in many ways. Some are common, some are rare, and together they make a large and uneven web of connections. Rohon’s web is much smaller and tighter. The same words keep returning in the same short sequences.

If you read it as language, it feels like a few phrases are being repeated over and over with small changes. Word pairs often appear in both directions, which almost never happens in natural texts. A few very common words link almost everything, while the rest depend on them. That gives the whole structure a rigid, formula-like shape instead of the messy balance seen in human speech or writing.

This kind of pattern might come from a cipher, a ritual formula, or simply an invented system that imitates language. Even compared with the Voynich manuscript, which already looks unnatural, Rohon’s structure is too neat and too repetitive. It has the shape of writing but not the behaviour of real words.

Here are the main KPI results commented:

Vocabulary size (nodes): Rohon has far fewer distinct words than normal texts. Its network is small, meaning the same words are used again and again instead of a wide vocabulary.

Clustering coefficient ©: It is much higher than usual. Words that appear together tend to form tight closed groups, showing fixed repeated phrases instead of flexible grammar.

Reciprocity: It is about three times higher than in normal languages. That means word pairs appear very often in both directions (“A B” and “B A”), which is rare in natural texts.

Type–Token Ratio (TTR): Very low. Few unique words compared to total word count. It confirms strong repetition.

Assortativity (assort_degree): Negative and stronger than average. Rare words link mostly to very common ones, giving the network a hub-and-spoke structure rather than the balanced pattern of natural language.

Betweenness centrality and eigenvector concentration: Both are high. A few central words control most connections, showing a rigid core instead of a spread of shared links.

Path length (L): Shorter than normal. Words are close to each other through those central hubs, another sign of repetitive structure.

Entropy measures (H_mean, H1): They show the text has moderate unpredictability but still less variety than human writing.