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arXiv · 2609.19306

Laplacian U-Processes for Multiple Change-Point Detection in Dependent Text Networks: An Application to Historical Chinese Articles

Fanghua Chen·Yizhou Cai·Lu zhou·Ting Fung Ma
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Abstract

We propose a two-view weighted-concordance operator (WCO) framework for offline change-point detection in weakly dependent text networks. Weighted word-co-occurrence graphs yield first-order direct-co-occurrence and second-order shared-context views, projected into paired Euclidean observations using maps learned from an independent pilot corpus. A WCO scan detects changes in cross-view dependence. A dependent multiplier bootstrap tests for a change, while a segment-wise block bootstrap gives a descriptive stability interval for its location. We establish null weak convergence, bootstrap validity, asymptotic size control, consistent localization under a single identifiable change, and perturbation bounds for token-level errors. CUSUM and Gaussian-kernel MMD scans provide complementary benchmarks. In the 1915-1921 New Youth corpus, the method detects a change in November 1919, with a descriptive 95 percent stability interval from April 1919 to June 1920, consistent with the linguistic transition surrounding China's May Fourth and New Culture movements.

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Fanghua Chen, Yizhou Cai, Lu zhou, Ting Fung Ma. 2026-09-16. Laplacian U-Processes for Multiple Change-Point Detection in Dependent Text Networks: An Application to Historical Chinese Articles. https://arxiv.org/abs/2609.19306

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