Average shortest-path length in word-adjacency networks: Chinese versus English

TL;DR

The paper investigates how punctuation and language shape the global topology of word-adjacency networks in Chinese and English. It constructs both token and word-only networks from a large Chinese corpus and selected translations, treating punctuation as nodes, and models the evolution of the average shortest path length $L(N)$ using an accelerated-growth framework with a sigmoid interpolation between chain-like and random-graph regimes. Key findings show that punctuation reduces $L(N)$ and that this effect is more pronounced for Chinese, while the networks exhibit a hierarchical, scale-free structure with $\alpha\approx 2$; translations induce only modest topological changes and preserve similar asymptotic $L(N)$. These results highlight punctuation’s significant topological role and offer a framework for cross-linguistic stylometry and readability analyses, with potential applications in text classification and linguistic network modeling.

Abstract

Complex networks provide powerful tools for analyzing and understanding the intricate structures present in various systems, including natural language. Here, we analyze topology of growing word-adjacency networks constructed from Chinese and English literary works written in different periods. Unconventionally, instead of considering dictionary words only, we also include punctuation marks as if they were ordinary words. Our approach is based on two arguments: (1) punctuation carries genuine information related to emotional state, allows for logical grouping of content, provides a pause in reading, and facilitates understanding by avoiding ambiguity, and (2) our previous works have shown that punctuation marks behave like words in a Zipfian analysis and, if considered together with regular words, can improve authorship attribution in stylometric studies. We focus on a functional dependence of the average shortest path length $L(N)$ on a network size $N$ for different epochs and individual novels in their original language as well as for translations of selected novels into the other language. We approximate the empirical results with a growing network model and obtain satisfactory agreement between the two. We also observe that $L(N)$ behaves asymptotically similar for both languages if punctuation marks are included but becomes sizably larger for Chinese if punctuation marks are neglected.

Figures (12)

• Figure 1: Word and punctuation-mark adjacency networks for The Drunkard in Chinese and English. For each text, the left column corresponds to networks created from the first 1,000 unique words and punctuation marks, the middle column corresponds to networks created from the entire books, and the right column represents the node-degree distributions calculated for the entire books.
• Figure 2: Word and punctuation-mark adjacency networks for Soul Mountain in Chinese and English. For each text, the left column corresponds to networks created from the first 1,000 unique words and punctuation marks, the middle column corresponds to networks created from the entire books, and the right column represents the node-degree distributions calculated for the entire books.
• Figure 3: Word and punctuation-mark adjacency networks for The Sun Shines over the Sanggan River in Chinese and English. For each text, the left column corresponds to networks created from the first 1,000 unique words and punctuation marks, the middle column corresponds to networks created from the entire books, and the right column represents the node-degree distributions calculated for the entire books.
• Figure 4: Average shortest path length $L(N)$ as a function of network size $N$ for a set of individual texts (grey dashed lines) together with mean ASPL $\langle L(N) \rangle$ averaged over all the texts (solid black) and the model fitted to it (solid red). Three sets are shown: 18 novels from the Late Qing era and early republican era till 1911 (top), 11 novels from the republican era 1912-1948 (upper middle), 10 novels from the Maoist era 1949-1978 (lower middle), and 23 contemporary Chinese novels since 1979 (bottom). The texts with punctuation marks (left) and without punctuation marks (right) are shown separately. Each empirical function has been averaged over networks obtained by shifting the starting point of the text by a fixed number of words. Note an extended scale of the vertical axis in (b).
• Figure 5: Average shortest path length $L(N)$ as a function of network size $N$ for a set of individual texts (grey dashed lines) together with the ASPL averaged over all the texts (solid black) and the hybrid model fitted to the average ASPL (solid red). Three sets are shown: 15 Internet novels (top), 8 novels written by Hong Kong writers (middle), and 9 novels written by Taiwanese writers (bottom). The texts with punctuation marks (left) and without punctuation marks (right) are shown separately. Each empirical function has been averaged over networks obtained by shifting the starting point of the text by a fixed number of words.