文献类型：期刊文献

英文题名：Regional analysis of study abroad search indices in China based on visibility graph theory: temporal patterns and regional coordination

作者：Wang, Li[1,2];Ma, Jun-Chao[2,3]

机构：[1]East China Univ Sci & Technol, Int Elite Engn Sch, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Res Ctr Econophys, Shanghai, Peoples R China;[3]East China Univ Sci & Technol, Sch Business, Shanghai, Peoples R China

年份：2026

卷号：14

外文期刊名：FRONTIERS IN PHYSICS

收录：;EI(收录号：20263221265474);Scopus(收录号：2-s2.0-105046602966);WOS:【SCI-EXPANDED(收录号:WOS:001819589300001)】；

语种：英文

外文关键词：educational mobility; regional analysis; regional coordination; search index; study abroad; visibility graph

摘要：Introduction Understanding the temporal dynamics and regional variation of study-abroad search attention is important for interpreting educational mobility intentions in a highly digitized information environment.Methods This study applies visibility graph theory to Douyin/Juliang Suanshu study-abroad search indices across 31 mainland Chinese provincial-level units from 4 June 2022 to 31 May 2025. Provincial series are aggregated into seven major regions, with summation used as the primary aggregation method and PCA used for aggregation sensitivity analysis. Regional natural visibility graphs are benchmarked against 100 size- and density-matched random graphs, alternative degree distributions are fitted, and regional network complexity is evaluated using the entropy weight method (EWM) with bootstrap uncertainty.Results The results show that all regional visibility graphs have substantially higher clustering than random benchmarks and small-world coefficients above 42, while the degree distributions are better interpreted as heavy-tailed than as uniquely confirmed power laws. Regional time series are strongly synchronized, with a mean off-diagonal zero-lag correlation of 0.987 and no systematic lead-lag pattern within a 30-day window. EWM ranks Central China highest in the daily analysis, followed by East China and South China, but bootstrap intervals overlap and the ordering is sensitive to weekly aggregation. Weekly visibility-graph community detection identifies 5-7 temporal communities per region and recurring transition dates around February 2023, September 2023, March 2024, and late 2024.Discussion These findings clarify the temporal organization of study-abroad search attention and provide a network-based framework for analyzing regional educational search behavior.