Abstract
Nowadays, more and more people realize the importance of patent for innovation activities. Patent citation network analysis is one of the most important methods for patent measurement, patent mining, and core patent identification. In nowadays, finding technology trajectories and analyzing major technologies in patent networks are intensively used in technological competition. Main path analysis (MPA) is a famous directed graph-based method to extract main paths in certain networks, such as a citation network. However, the accuracy of main path identification may be distracted due to a large volume of wrong references when using MPA in patent citation networks solely. To tackle this challenge and extract reasonable main paths from patent citation network, in this paper, we combined the classic MPA with the PageRank algorithm and we tested this new combined method on authorized patent datasets. The results show that the improved method achieved better performance in average cited frequency and other indicators of core patents comparing with traditional MPA.
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Acknowledgement
I would like to thank professor Mei Song for her important comments on several drafts of this work. I thank associate professor Xiaojuang Wang for the stimulating and meaningful guidance she made as a reviewer. I gratefully acknowledge help from my parents, they always encourage me when I am frustrated, so I can finish this work.
Funding
This work was supported by the National Natural Science Foundation of China (61601053).
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Lu, Z., Ma, Y., Song, L. (2021). Patent Citation Network Analysis Based on Improved Main Path Analysis: Mapping Key Technology Trajectory. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-78618-2_13
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DOI: https://doi.org/10.1007/978-3-030-78618-2_13
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