Exploring Word-Adjacency Networks with Multifractal Time Series Analysis Techniques

Area of Science:

• Quantitative Linguistics
• Network Science
• Complex Systems Analysis

Background:

• Traditional linguistic analysis often overlooks the intricate structural patterns within texts.
• Understanding the complex organization of language is crucial for advancements in computational linguistics and network theory.

Purpose of the Study:

• To introduce a novel method for exploring linguistic networks by mapping word-adjacency networks to time series.
• To apply multifractal analysis techniques to uncover complex structural patterns in textual data.
• To investigate the impact of punctuation on linguistic network analysis.

Main Methods:

• Mapping word-adjacency networks to time series data.
• Applying multifractal analysis to temporal sequences derived from network properties (clustering coefficients, node degrees).
• Case study using Lewis Carroll's "Alice's Adventures in Wonderland" with and without punctuation.

Main Results:

• Time series derived from clustering coefficients exhibit multifractal characteristics, indicating inherent textual complexity.
• Statistical validation confirmed the multifractal properties are genuine, not spurious.
• Incorporating punctuation altered the scaling to a non-uniform multifractal form; node degree analysis showed less complexity.

Conclusions:

• The proposed method offers a new perspective for quantitative linguistics and network science.
• Multifractal analysis of linguistic networks reveals deep insights into text structure.
• The study highlights the significant, yet complex, role of punctuation in textual organization.