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OA

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.