China's Shrinking Home Bias and Rising Disruptive Impact: Evidence from a Global Citation Network Analysis

Nan Deng; Zheng Ma; Xiaohui Liu

Home/Archive/arXiv

arXiv · 2608.28139

China's Shrinking Home Bias and Rising Disruptive Impact: Evidence from a Global Citation Network Analysis

Nan Deng·Zheng Ma·Xiaohui Liu

View original source ↗Read source document ↗Export metadata ↓

Abstract

China has become the world's largest producer of scientific publications, yet concerns persist that this growth is inflated by excessive domestic citation practices. In this study, we analyze a citation network of over 45 million publications from Web of Science (1980-2025) to investigate China's home citation bias and research impact. Using a network reshuffling null model to control for the structural effect of publication volume, we find that China's home citation bias is less pronounced than commonly assumed and has been steadily declining over the past two decades. Chinese researchers do not exhibit a significantly stronger home citation preference than other major countries, indicating increasing internationalization rather than insularity. Furthermore, using the persistent disruption framework, we show that Chinese papers are converging toward American papers in their capacity to produce paradigm-shifting work. These findings challenge prevailing narratives about Chinese scientific home bias and suggest that China's advances in research impact.

Explore related subjects

physics.soc-ph cs.DL

Keep this discovery

BibTeX RIS

Nan Deng, Zheng Ma, Xiaohui Liu. 2026-08-31. China's Shrinking Home Bias and Rising Disruptive Impact: Evidence from a Global Citation Network Analysis. https://arxiv.org/abs/2608.28139

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.DL subject physics.soc-ph subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

SoniMet - A tool for sonifying and visualizing the performance of single researchers

For centuries, the scientific community has predominantly relied on visual tools to communicate empirical results and complex datasets. While visual representations dominate bibliometric analyses, the human auditory system possesses sensitive capacities for processing complex temporal information, distinguishing intricate patterns, and tracking parallel data streams. Data sonification translates data relations into acoustic signals, offering an alternative method for data exploration, pattern recognition, and scientific communication. This paper applies this concept to the field of bibliometrics through metrics sonification-the auditory translation of bibliometric information-and introduces SoniMet (Sonifying Metrics), a web-based tool designed to visualize and sonify the publication and citation data of individual scientists (see https://sonimet.kennebec.co.uk). SoniMet connects to the OpenAlex database to retrieve bibliometric records and displays them on an interactive chronological timeline. The tool employs direct parameter mapping to translate citation impact indicators into non-speech sound: field-weighted citation impact and citation counts determine the pitch and volume of a synthesized note and are mapped to the acoustic echo strength. Although SoniMet expands the methodological toolkit for research evaluation, current limitations include its restriction to individual scholar profiles and the challenge of integrating transient audio files into traditional, text-based scientific publishing workflows. Future empirical user studies are necessary to systematically evaluate the analytical utility and cognitive benefits of metrics sonification compared to established visual methods.

cs.DL↗

arXiv2026

Functional Connectivity Networks for Transportation Delay Analysis: from Theory to Software

Within the endeavour of modelling and understanding the propagation of delays in transportation networks, an approach that has attracted increasing interest in the last decade is the creation of functional network representations. These graphs map elements of interest (e.g. airports or stations) as nodes, and derive pairwise propagation patterns from their dynamics through correlation and causality tests. In spite of multiple notable results, this approach still lacks a coherent framework, with decisions related to many fundamental steps being left to the judgement of the researcher. We here provide an introduction to the theory behind functional networks for transportation systems, detailing the main steps and the associated pitfalls. We further introduce a Python package, delaynet, designed to support the researcher in the reconstruction and analysis of such networks. We finally present an analysis of the propagation of delays in the Swiss train system; and discuss future research steps.

physics.soc-ph↗

arXiv2026

Mitigating Disease Spread by Design in Refugee and IDP Camps

Disease spread represents an increasing challenge in refugee and internally displaced person (IDP) settlements. The movement and interaction of people within camps is influenced by their layout, which therefore has the potential to significantly affect disease spread. This work aims at creating a methodology to explore the potential effects of different camp layouts as mitigating factors in the spread of diseases within settlements. We showcase proof-of-concept experiments by leveraging the JUNE agent-based epidemic model, discuss the kind of operational insights this methodology can facilitate, and provide a framework for future investigations.

physics.soc-ph↗