Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks

Nakai, Kensei; Nonaka, Hirofumi; Hentona, Asahi; Kanai, Yuki; Sakumoto, Takeshi; Kataoka, Shotaro; Carreón, Elisa Claire Alemán; Hiraoka, Toru

doi:10.1109/ieem.2018.8607487

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Community structures in citation networks for scientific articles have been analyzed recently (see, for example, Refs [17][18][19]), and opportunities for similar studies on the available large amounts of rich patent-citation data are now also beginning to be realized [20,21]. Our study offers an example for the type of interesting insights that can be gained by applying network-analysis-based community-detection methods in the important area of technical innovation.…”

Section: Introductionmentioning

confidence: 92%

Community structure in co-inventor networks affects time to first citation for patents

et al. 2019

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We have investigated community structure in the co-inventor network of a given cohort of patents and related this structure to the dynamics of how these patents acquire their first citation. A statistically significant difference in the time lag until first citation is linked to whether or not this citation comes from a patent whose listed inventors share membership in the same communities as the inventors of the cited patent. Although the inventor-community structures identified by different community-detection algorithms differ in several aspects, including the community-size distribution, the magnitude of the difference in time to first citation is robustly exhibited. Our work is able to quantify the expected acceleration of knowledge flow within inventor communities and thereby further establishes the utility of network-analysis tools for studying innovation dynamics.

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Section: Introductionmentioning

confidence: 92%

Community structure in co-inventor networks affects time to first citation for patents

et al. 2019

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“…In recent years, community detection techniques have been used in the detection of technology clusters or scientific knowledge clusters based on the network of patent or scientific publications [14][15][16][17]. e community detected in the citation network of patent or scientific publications is different from the community of innovation.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Visualizing the Communities of Innovation in Beijing-Tianjin-Hebei Urban Agglomeration Based on the Patent Cooperation Network

Zhou

Zhang

2021

Complexity

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For a deep understanding of Beijing-Tianjin-Hebei (BTH) collaborative innovation, we detected and visualized the communities of innovation in BTH Urban Agglomeration based on the patent cooperation network. China Patent Database was connected with Business Registration Database and the Tianyan Check to achieve the geographical information of organizational innovators. Spinglass algorithm was applied and ultimately 12 communities of innovation were detected. Based on the different structure characteristics, we further clustered the 12 communities into four typical structures that are hierarchical, single-center, polycentric, and flat structures. The hierarchical structure is usually large in scale and the cooperative intensity is relatively high. Single-center structure has a center with a high proportion of centrality and the cooperative intensity is relatively low. Polycentric structure has multiple centers with similar proportions of centrality. Flat structure is usually small in scale and has no obvious network center. In the patent cooperative network of BTH Urban Agglomeration, universities and state-owned enterprises occupied the centers and acted important roles to connect other organizations. Some communities of innovation showed significant industry characteristics, mainly involving six industry fields that are electric power, construction, petroleum, metallurgy and materials, municipal transportation, and railway. From the geographical perspective, some communities manifested local attributes and some demonstrated cooperation between regions. Beijing was the center of the Beijing-Tianjin-Hebei patent cooperation network. Compared with the pair of Beijing-Tianjin and the pair of Beijing-Hebei, Tianjin and Hebei were not closely connected. In the future, Beijing-Tianjin-Hebei collaborative innovation should strengthen cooperation between Tianjin and Hebei.

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Patent Semantic Community Detection Based on Game Theory

Sun

Shi

Liu

2020

Advances in Intelligent Systems and Computing

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Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks

Cited by 5 publications

References 18 publications

Community structure in co-inventor networks affects time to first citation for patents

Community structure in co-inventor networks affects time to first citation for patents

Detecting and Visualizing the Communities of Innovation in Beijing-Tianjin-Hebei Urban Agglomeration Based on the Patent Cooperation Network

Patent Semantic Community Detection Based on Game Theory

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