物理学报2026，Vol.75Issue(10)：128-144,17.DOI:10.7498/aps.75.20251802
面向技术创新系统的专利数据集构建:跨尺度结构对比与演化机制研究
A multi-scale patent dataset for technological innovation systems:Cross-scale structural comparison and evolutionary mechanisms
摘要
Abstract
Scale dependence is a pervasive feature of socioeconomic networks:even when generated by the same class of economic activities,networks observed at different levels of entity aggregation can exhibit markedly different structural organizations,yet a systematic and testable explanation for such cross-scale divergence remains lacking.This paper presents a U.S.patent dataset together with a unified analytical framework that combines empirical network analysis with mechanism-based modeling to quantify and interpret structural differences across scales.The dataset contains 1,225,373 granted USPTO utility patents filed during 2000-2020 and integrates assignee geography(state/county/city),firm identifiers,CPC classification codes,and patent texts;technologies are defined at the 4-digit CPC level.To measure technology activity when patents involve multiple technologies,we use co-citation information to allocate each patent's technological shares across its associated CPC codes,thereby obtaining technology-share weights beyond naive equal counting.Using these weights,we construct entity-technology bipartite networks at four scales(state,county,city,and firm)and derive two technology space networks,one based on technology co-occurrence across entities and the other based on patent-text similarity.We characterize network structure using bipartite modularity(Q),global nestedness(N),and in-block nestedness(I),and evaluate statistical significance against degree-constrained null models based on the bipartite configuration model(BiCM).Empirically,modularity increases as the entity scale becomes finer;state-and county-level networks are closer to a globally nested organization,whereas city-and firm-level networks exhibit a pronounced shift toward in-block nestedness.Temporal analysis further shows that the formation of new entity-technology links reflects a scale-dependent balance between preference for globally central technologies and reliance on relatedness density to the existing technological portfolio,with smaller-scale entities exhibiting a stronger dependence on relatedness density.Finally,we propose an evolutionary model that incorporates both relatedness-density preference and technology-centrality preference under empirical degree constraints.Simulations demonstrate that tuning these two preferences reproduces the observed transition from global nestedness to in-block nestedness,providing a mechanism-based explanation for scale-dependent structural patterns in technological innovation networks.The dataset presented in this paper is openly available at https://doi.org/10.57760/sciencedb.j00213.00265.关键词
专利数据/技术创新系统/复杂网络/嵌套性/模块化Key words
patent data/technological innovation systems/complex networks/nestedness/modularity引用本文复制引用
黄羿炜,徐舒琪,吕琳媛..面向技术创新系统的专利数据集构建:跨尺度结构对比与演化机制研究[J].物理学报,2026,75(10):128-144,17.基金项目
国家自然科学基金重大项目(批准号:T2293771)和国家自然科学基金青年科学基金(批准号:62306191)资助的课题. Project supported by the Major Program of the National Natural Science Foundation of China(Grant No.T2293771)and the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.62306191). （批准号:T2293771）
