The means of the top 10, the middle 10, and the final 10 degree-dependent clustering coefficients.

The means of the top 10, the middle 10, and the final 10 degree-dependent clustering coefficients.</p

How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks

<div><p>Language is a complex adaptive system, but how does it change? For investigating this process, four diachronic Chinese word co-occurrence networks have been built based on texts that were written during the last 2,000 years. By comparing the network indicators that are associated with the hierarchical features in language networks, we learn that the hierarchy of Chinese lexical networks has indeed evolved over time at three different levels. The connections of words at the micro level are continually weakening; the number of words in the meso-level communities has increased significantly; and the network is expanding at the macro level. This means that more and more words tend to be connected to medium-central words and form different communities. Meanwhile, fewer high-central words link these communities into a highly efficient small-world network. Understanding this process may be crucial for understanding the increasing structural complexity of the language system.</p></div

MUL distributions (TP1 corresponds to Network 1 text).

MUL distributions (TP1 corresponds to Network 1 text).</p

The diachronic change of the MUL of Chinese (measured based on words) and the MWL of Chinese (measured based on characters).

<p>The diachronic change of the MUL of Chinese (measured based on words) and the MWL of Chinese (measured based on characters).</p

Diachronic change of network parameters.

<p>Diachronic change of network parameters.</p

Evolution of the clustering coefficient (<i></i>) and the average path length (<i></i>).

<p>Evolution of the clustering coefficient (<i></i>) and the average path length (<i></i>).</p

Power-law fitting (dual-log) of average degree of nearest neighbors of four diachronic Chinese networks.

Power-law fitting (dual-log) of average degree of nearest neighbors of four diachronic Chinese networks.</p

Power-law fitting (dual-log) of degree distributions of four diachronic Chinese networks.

<p>Power-law fitting (dual-log) of degree distributions of four diachronic Chinese networks.</p

A word-form co-occurrence network based on an English paragraph.

A word-form co-occurrence network based on an English paragraph.</p

Power-law fitting of the degree-dependent clustering coefficients of four diachronic Chinese networks.

<p>Power-law fitting of the degree-dependent clustering coefficients of four diachronic Chinese networks.</p