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Heatmap of citations. Notes: CPC 1 -and 2 digit categories. A-Human Necessities A0-Agriculture. A2-Foodstuffs; Tobacco. A4-Personal or Domestic Articles. A6-Health; Life-Saving; Amusement. A9-Miscellaneous, of Human Necessities. B-Performing Operations; Transporting. B0-Separating; Mixing. B2-Shaping. B3-Shaping. B4-Printing. B5-Transporting. B6-Microstructural Technology; Nanotechnology. B9-Miscellaneous, Of Performing Operations; Transporting. C-Chemistry; Metallurgy. C0Chemistry. C2-Metallurgy. C3-Metallurgy. C4-Combinatorial Technology. C9-Miscellaneous, of Chemistry; Metallurgy. D-Textiles; Paper. D0-Textiles or Flexible Materials not Otherwise Provided for. D2-Paper. D9-Miscellaneous, of Textiles; Paper. E-Fixed Constructions. E0-Building. E2-Earth or Rock Drilling; Mining. E9-Miscellaneous, Of Fixed Constructions. F-Mechanical Engineering; Lighting; Heating; Weapons; Blasting. F0-Engines or Pumps. F1-Engineering in General. F2-Lighting; Heating. F4-Weapons; Blasting. F9-Miscellaneous, of Mechanical Engineering; etc. G-Physics. G0-Measuring; Optics; Horology; Controlling; Computing; Signaling. G1-Acoustics; Information Storage; Instruments; ICT Adapted to Applications. G2-Nuclear Physics; Nuclear Engineering. G9-Miscellaneous, of Physics. Each value in the cells is row normalized.
Source publication
How does technological interdependence affect innovation? We address this question by examining the influence of neighbors' innovativeness and the structure of the innovators' network on a sector's capacity to develop new technologies. We study these two dimensions of technological interdependence by applying novel methods of text mining and networ...
Context in source publication
Context 1
... coefficient (M 0.893, SD 0.021) and a low (unweighted) average shortest path length (M 1.217, SD 0.046). The average normalized degree is 0.746 (SD 0.241), while the average total similarity score of each sector (average weighted degree) is 20.809 (SD 8.616). This information on the network structure is not reported in Table 2 for brevity. In Figs. 1 and 2, we show the heatmaps of the pairwise correlation across sectors in terms of citation flows and text similarity, obtained considering the entire time span between 1976 and 2021. The full list of 128 technology classes (3-digit level) is reported on the bottom horizontal and the right-hand vertical axes. The corresponding 2-digit ...
Citations
... Patent data provide a reliable means to measure different aspects of both individual and collective economic behaviors (e.g., Griliches 1990, Nagaoka et al. 2010, Kogan et al. 2017. Patent citation data specifically has been shown to be informative about the importance of individual patents, future corporate R&D activity and stock market valuation (Trajtenberg 1990, Hall et al. 2005, Fronzetti Colladon et al. 2025, as well as highly associated to domain level technological improvement rates (Benson and Magee 2015, Magee et al. 2016, Triulzi et al. 2020, Singh et al. 2021). ...
... The structure of patent citation networks has a self-referencing effect on the citations a patent receives (forward citations). For example, an inclination towards transitivity (citing references of references) has been found (An andDing 2018, Chakraborty et al. 2020), while some patents are more important than other patents in the development of a domain (Fronzetti Colladon et al. 2025). These effects have been found with the usage of exponential random graph models (ERGM), which where specifically developed to estimates autocorrelation effects of network structures (Holland where the drop probability is largest, and most impact full. ...
... Importance of patents as measured by average degree centrality affects technology improvement rate (Benson and Magee 2015). In (Fronzetti Colladon et al. 2025) the authors include a composite of centrality measures in mapping technological independence of patents, including Katz centrality (Katz 1953), degree centrality, betweennness centrality, closeness centrality (Freeman 1977) and distinctiveness (Fronzetti Colladon and Naldi 2020). We do see value in that approach when assessing technological independence, yet in our model it would introduce ambiguity. ...
We explore a dynamic patent citation network model to explain the established link between network structure and technological improvement rate. This model, a type of survival model, posits that the *dynamic* network structure determines the *constant* improvement rate, requiring consistent structural reproduction over time. The model's hazard rate, the probability of a patent being cited, represents "knowledge production," reflecting the output of new patents given existing ones. Analyzing hydrogen technology patents, we find distinct subdomain knowledge production rates, but consistent development across subdomains. "Distribution" patents show the lowest production rate, suggesting dominant "distribution" costs in pricing. Further modeling shows Katz-centrality predicts knowledge production, outperforming subdomain classification. Lower Katz centrality in "distribution" suggests inherent organizational differences in invention. Exploitative learning (within-subdomain citations) correlates with higher patenting opportunity costs, potentially explaining slower "distribution" development, as high investment needs may incentivize monopolization over knowledge sharing.
This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five Distinctiveness measures are considered, as well as a more meaningful range of the α parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.
This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five distinctiveness measures are considered, as well as a more meaningful range of the {\alpha} parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.
