在現今的知識經濟時代，專利成為了企業和研究機構保護創新成果、維護競爭優勢的重要手段，對於專利進行路徑分析因此具有重要意義，專利不僅僅代表著技術和科學上的進展，更是企業和研究機構在特定領域中價值的體現，潛藏著對於特定領域的關鍵資訊、技術路徑和創新方向，然而專利路徑研究相對於其他領域的研究來說仍然相對較少，因此對於專利進行深入的分析和研究具有極其重要的意義和價值。
本研究基於中心性指標定義一種新的尋找主要路徑的方法，專門應用於專利數據的研究。在初步進行USPTO專利的主路徑分析(main path analysis)和最長路徑分析(longest path analysis)時，我們發現到在特定情況下這些方法的效果並不符合預期，為了改進主路徑分析結果的準確性和有效性，我們開展了進一步的研究，旨在結合中心性指標和路徑分析技術，探索一種新的方法來識別在USPTO專利中具有重要意義的路徑。
這種基於中心性指標的路徑分析方法充分考慮了專利網路中節點的重要性和連結的影響，以揭示潛藏在專利數據中的關鍵路徑。我們通過計算不同中心性指標（例如:Eigenvector centrality、PageRank centrality、Katz centrality）來評估每個節點的重要性，並將這些指標應用於路徑分析中，以識別在專利網路中具有關鍵功能和重要性的路徑。為了評估我們所提出方法的可行性，我們將其與主要路徑分析和最長路徑方法進行了比較，並分析了這些方法在發現關鍵重要路徑方面的差異和優勢。

In today's knowledge-based economy, patents have become a crucial means for businesses and research institutions to protect their innovative achievements and maintain competitive advantages. Conducting pathway analysis on patents holds significant importance. Patents not only represent technological and scientific advancements but also embody the value of enterprises and research institutions in specific domains. They hold crucial information, technological pathways, and innovation directions. However, compared to other fields of research, the study of patents remains relatively limited. Therefore, conducting in-depth analysis and research on patents holds immense significance and value.
This study proposes a pathway analysis method based on centrality measures specifically designed for patent data research. During the initial exploration of USPTO patents using main path analysis and longest path analysis, we recognized that these methods did not always yield the expected outcomes in specific scenarios. To improve the accuracy and effectiveness of our analyses, we conducted further research aimed at combining centrality measures with pathway analysis techniques to explore a novel approach for identifying significant pathways within USPTO patents.
This centrality-based pathway analysis method takes into consideration the importance of nodes and the impact of connections in the patent network, aiming to reveal critical pathways hidden within the patent data. We evaluate the importance of each node by calculating various centrality measures such as Eigenvector centrality, PageRank, and Katz centrality. These measures are then applied to pathway analysis to identify pathways with crucial functionality and significance within the patent network. To assess the performance of our proposed method, we compare it with main path analysis and longest path analysis methods, analyzing the differences and advantages in discovering critical pathways.

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 vi
表目錄 vii
壹、 緒論 1
1.1 動機與目的 1
1.2 研究貢獻 3
1.3 論文架構 4
貳、 重複自我引用探討 5
2.1 重複自我引用問題探討 5
2.2 Centrality Path Score 7
2.3 Main path++ algorithm 9
2.4 實際例子 12
參、 實驗與結果 14
3.1 資料集 14
3.2 實驗工具 16
3.3 實驗方式 17
3.4 實驗結果與分析 21
肆、 相關研究探討 30
伍、 結論與未來展望 32
附錄一、Centrality 33
附錄二、Main path analysis 43
參考文獻 55

[1] V. Batagelj and A. Mrvar, “Pajek — Analysis and Visualization of Large Networks,” Springer Berlin Heidelberg, pp. 77-103, 2004.
[2] N. P. Hummon and P. Dereian, “Connectivity in a citation network: The development of DNA theory,” Social Networks, vol. 11, pp. 39-63, no. 1, 1989.
[3] V. Batagelj, Efficient Algorithms for Citation Network Analysis. Wiley Publishing, 2003.
[4] J. S. Liu and L. Y. Y. Lu, “An integrated approach for main path analysis: Development of the Hirsch index as an example,” J. Assoc. Inf. Sci. Technol, vol. 63, pp. 528-542, 2012.
[5] F. Han, S. Yoon, N. Raghavan, B. Yang and H. Park, “Technological trajectory in fuel cell technologies: A patent-based main path analysis,” International Journal of Hydrogen Energy, vol. 50, pp. 1347-1361, 2024.
[6] R. L. T. Cho, J. S. Liu and M. H. C. Ho, “Autonomous Vehicle Technology Development: A Patent Survey Based on Main Path Analysis,” Portland International Conference on Management of Engineering and Technology (PICMET), pp. 1-9, 2019.
[7] M. Szomszor, D. A. Pendlebury and J. Adams, “How much is too much? The difference between research influence and self-citation excess,” Scientometrics, vol. 123, pp. 1119-1147, 2020.
[8] Z. Gu, J. Liu, K. Cao, J. Zhang and J. Wang, “Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes,” BMC Syst Biol, vol. 6, pp. 56, 2012.
[9] W. M. J. Mohammed J. Zaki, Data Mining and Analysis Fundamental Concepts and Algorithms. Cambridge University Press, 2014.
[10] M. E. J. Newman, The mathematics of networks. Palgrave Macmillan Press, 2008.
[11] C. F. A. Negre, U. N. Morzan, H. P. Hendrickson, R. Pal, G. P. Lisi and J. P. Loria, “Eigenvector centrality for characterization of protein allosteric pathways,” Proc Natl Acad Sci USA, vol. 115, no. 52, pp. E12201- E12208, 2018.
[12] J. M. Fletcher and T. Wennekers, “From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity,” Int J Neural Syst, vol. 28, no. 2, pp. 1750013-1 - 1750013-16, 2018.
[13] D. Austin, “How Google Finds Your Needle in the Web's Haystack,” Retrieved from https://www.ams.org/publicoutreach/feature-column/fcarc-pagerank, 2006.
[14] P. Bonacich, “Factoring and weighting approaches to status scores and clique identification,” The Journal of Mathematical Sociology, vol. 2, no. 1, pp. 113-120, 1972.
[15] C. Cruz, J. Labonne and P. Querubín, “Politician Family Networks and Electoral Outcomes: Evidence from the Philippines,” The American Economic Review, vol. 107, no. 10, pp. 3006-3037, 2017.
[16] L. Page, S. Brin, R. Motwani and T. Winograd, “The PageRank Citation Ranking : Bringing Order to the Web,” in The Web Conference, 1999.
[17] S. Brin and L. Page, “The anatomy of a large-scale hypertextual Web search engine,” Computer Networks and ISDN Systems, vol. 30, no. 1, pp. 107-117, 1998.
[18] T. P. Chartier, “A Googol of Information about Google,” Computing in Science & Engineering, vol. 10, pp. 11-12, 2008.
[19] Y. Jing and S. Baluja, “VisualRank: Applying PageRank to Large-Scale Image Search,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1877-1890, 2008.
[20] L. Katz, “A new status index derived from sociometric analysis,” Psychometrika, vol. 18, no. 6, pp. 39-43, 1953.
[21] R. Hanneman, Introduction to Social Network Methods. University of California Press, 2005.
[22] B. H. Junker and F. Schreiber, Analysis of Biological Networks. Wiley Publishing, 2007.
[23] M. Newman, Networks: An Introduction. Oxford University Press, 2010.
[24] P. Laflin, A. V. Mantzaris, F. Ainley, A. Otley, P. Grindrod and D. J. Higham, “Discovering and validating influence in a dynamic online social network,” Social Network Analysis and Mining, vol. 3, no. 4, pp. 1311-1323, 2013.
[25] J. Park and M. E. J. Newman, “A network-based ranking system for US college football,” Journal of Statistical Mechanics: Theory and Experiment, pp. 1-15, 2005.
[26] L. A. Diana and L. Leydesdorff, “Main-path analysis and path-dependent transitions,” Journal of the American Society for Information Science and Technology, 2008.
[27] C. Colicchia and F. Strozzi, “Supply chain risk management: a new methodology for a systematic literature review,” Supply Chain Management: An International Journal, vol. 17, no. 4, pp. 403-418, 2012.
[28] L. Lu and J. Liu, “The Knowledge Diffusion Paths of Corporate Social Responsibility – From 1970 to 2011,” Corporate Social Responsibility and Environmental Management, vol. 21, 2014.
[29] H. Liang, J.-J. Wang, Y. Xue and X. Cui, “IT outsourcing research from 1992 to 2013: A literature review based on main path analysis,” Information & Management, vol. 53, no. 2, pp. 227-251, 2016.
[30] T. C. Chuang, J. S. Liu, L. Y. Y. Lu and Y. Lee, “The main paths of medical tourism: From transplantation to beautification,” Tourism Management, vol. 45, pp. 49-58, 2014.