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Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks

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  • Yu, Dejian
  • Sheng, Libo

Abstract

Main path analysis is a useful tool to form the backbone of a citation network by linking important connections, which has been widely used to track the knowledge diffusion paths in a specific domain. Contrary to the traditional assumption that all citations in the citation network are treated equally, this paper proposes the influence difference main path analysis model by distinguishing citations based on the prestige of citing papers. Three algorithms named search path count with PageRank (SPC-PR), search path link count with PageRank (SPLC-PR) and search path node pair with PageRank (SPNP-PR) are devised to weight the citation network in this paper. Finally, two cases, the DNA citation network and a large-scale citation network related to the blockchain domain, are investigated to examine the effectiveness of the proposed algorithms. The results show that the proposed model can not only uncover the evolutionary process appropriately, but also can effectively distinguish the citations in the network by taking the influence difference into account. This study enriches the methodology research of main path analysis and provides the scholars with practical reference for the further development of main path analysis.

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  • Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:4:s1751157721000572
    DOI: 10.1016/j.joi.2021.101186
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    as
    1. Wu, Shan & Tong, Mu & Yang, Zhongyi & Derbali, Abdelkader, 2019. "Does gold or Bitcoin hedge economic policy uncertainty?," Finance Research Letters, Elsevier, vol. 31(C), pages 171-178.
    2. Fang, Libing & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2019. "Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin?," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 29-36.
    3. Gkillas, Konstantinos & Katsiampa, Paraskevi, 2018. "An application of extreme value theory to cryptocurrencies," Economics Letters, Elsevier, vol. 164(C), pages 109-111.
    4. Dejian Yu & Libo Sheng, 2020. "Knowledge diffusion paths of blockchain domain: the main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 471-497, October.
    5. Giuffrida, Cristiano & Abramo, Giovanni & D’Angelo, Ciriaco Andrea, 2019. "Are all citations worth the same? Valuing citations by the value of the citing items," Journal of Informetrics, Elsevier, vol. 13(2), pages 500-514.
    6. Urquhart, Andrew, 2017. "Price clustering in Bitcoin," Economics Letters, Elsevier, vol. 159(C), pages 145-148.
    7. Bart Verspagen, 2007. "Mapping Technological Trajectories As Patent Citation Networks: A Study On The History Of Fuel Cell Research," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 93-115.
    8. Tiwari, Aviral Kumar & Jana, R.K. & Das, Debojyoti & Roubaud, David, 2018. "Informational efficiency of Bitcoin—An extension," Economics Letters, Elsevier, vol. 163(C), pages 106-109.
    9. Fiala, Dalibor & Tutoky, Gabriel, 2017. "PageRank-based prediction of award-winning researchers and the impact of citations," Journal of Informetrics, Elsevier, vol. 11(4), pages 1044-1068.
    10. Lai, Kuei-Kuei & Bhatt, Priyanka C. & Kumar, Vimal & Chen, Hsueh-Chen & Chang, Yu-Hsin & Su, Fang-Pei, 2021. "Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories," Journal of Informetrics, Elsevier, vol. 15(2).
    11. Charfeddine, Lanouar & Benlagha, Noureddine & Maouchi, Youcef, 2020. "Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors," Economic Modelling, Elsevier, vol. 85(C), pages 198-217.
    12. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 521-542, April.
    13. Daim, Tugrul & Lai, Kuei Kuei & Yalcin, Haydar & Alsoubie, Fayez & Kumar, Vimal, 2020. "Forecasting technological positioning through technology knowledge redundancy: Patent citation analysis of IoT, cybersecurity, and Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    14. Fry, John, 2018. "Booms, busts and heavy-tails: The story of Bitcoin and cryptocurrency markets?," Economics Letters, Elsevier, vol. 171(C), pages 225-229.
    15. Su, Chi-Wei & Qin, Meng & Tao, Ran & Umar, Muhammad, 2020. "Financial implications of fourth industrial revolution: Can bitcoin improve prospects of energy investment?," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    16. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    17. John S. Liu & Hsiao-Hui Chen & Mei Hsiu-Ching Ho & Yu-Chen Li, 2014. "Citations with different levels of relevancy: Tracing the main paths of legal opinions," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(12), pages 2479-2488, December.
    18. Koutmos, Dimitrios, 2018. "Return and volatility spillovers among cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 122-127.
    19. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    20. Ying Huang & Donghua Zhu & Yue Qian & Yi Zhang & Alan L. Porter & Yuqin Liu & Ying Guo, 2017. "A hybrid method to trace technology evolution pathways: a case study of 3D printing," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 185-204, April.
    21. John S. Liu & Louis Y. Y. Lu & Mei Hsiu-Ching Ho, 2019. "A few notes on main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 379-391, April.
    22. Yu, Dejian & Pan, Tianxing, 2021. "Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain," Journal of Informetrics, Elsevier, vol. 15(2).
    23. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    24. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    25. Zhang, Yu & Wang, Min & Gottwalt, Florian & Saberi, Morteza & Chang, Elizabeth, 2019. "Ranking scientific articles based on bibliometric networks with a weighting scheme," Journal of Informetrics, Elsevier, vol. 13(2), pages 616-634.
    26. Su, Chi-Wei & Qin, Meng & Tao, Ran & Shao, Xue-Feng & Albu, Lucian Liviu & Umar, Muhammad, 2020. "Can Bitcoin hedge the risks of geopolitical events?," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    27. Fen Zhao & Yi Zhang & Jianguo Lu & Ofer Shai, 2019. "Measuring academic influence using heterogeneous author-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1119-1140, March.
    28. Tanzila Ahmed & Ben Johnson & Charles Oppenheim & Catherine Peck, 2004. "Highly cited old papers and the reasons why they continue to be cited. Part II., The 1953 Watson and Crick article on the structure of DNA," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(2), pages 147-156, October.
    29. Kajtazi, Anton & Moro, Andrea, 2019. "The role of bitcoin in well diversified portfolios: A comparative global study," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 143-157.
    30. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    31. Fuli Zhang, 2017. "Evaluating journal impact based on weighted citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 1155-1169, November.
    32. Rico Lee-Ting Cho & John S. Liu & Mei Hsiu-Ching Ho, 2021. "The development of autonomous driving technology: perspectives from patent citation analysis," Transport Reviews, Taylor & Francis Journals, vol. 41(5), pages 685-711, September.
    33. Katsiampa, Paraskevi & Corbet, Shaen & Lucey, Brian, 2019. "High frequency volatility co-movements in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 35-52.
    34. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    35. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    36. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    37. Nykl, Michal & Ježek, Karel & Fiala, Dalibor & Dostal, Martin, 2014. "PageRank variants in the evaluation of citation networks," Journal of Informetrics, Elsevier, vol. 8(3), pages 683-692.
    38. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    39. Rainer Böhme & Nicolas Christin & Benjamin Edelman & Tyler Moore, 2015. "Bitcoin: Economics, Technology, and Governance," Journal of Economic Perspectives, American Economic Association, vol. 29(2), pages 213-238, Spring.
    40. Persson, Olle, 2010. "Identifying research themes with weighted direct citation links," Journal of Informetrics, Elsevier, vol. 4(3), pages 415-422.
    41. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    42. Zeng, Ting & Yang, Mengying & Shen, Yifan, 2020. "Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks," Economic Modelling, Elsevier, vol. 90(C), pages 209-220.
    43. Chen, Kaihua & Zhang, Yi & Fu, Xiaolan, 2019. "International research collaboration: An emerging domain of innovation studies?," Research Policy, Elsevier, vol. 48(1), pages 149-168.
    44. Mehrdokht Pournader & Yangyan Shi & Stefan Seuring & S.C. Lenny Koh, 2020. "Blockchain applications in supply chains, transport and logistics: a systematic review of the literature," International Journal of Production Research, Taylor & Francis Journals, vol. 58(7), pages 2063-2081, April.
    45. María de la O González & Francisco Jareño & Frank S. Skinner, 2020. "Nonlinear Autoregressive Distributed Lag Approach: An Application on the Connectedness between Bitcoin Returns and the Other Ten Most Relevant Cryptocurrency Returns," Mathematics, MDPI, vol. 8(5), pages 1-22, May.
    46. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.
    47. Platanakis, Emmanouil & Sutcliffe, Charles & Urquhart, Andrew, 2018. "Optimal vs naïve diversification in cryptocurrencies," Economics Letters, Elsevier, vol. 171(C), pages 93-96.
    48. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "Data envelopment analysis 1978–2010: A citation-based literature survey," Omega, Elsevier, vol. 41(1), pages 3-15.
    49. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    50. Ji, Qiang & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav, 2019. "Information interdependence among energy, cryptocurrency and major commodity markets," Energy Economics, Elsevier, vol. 81(C), pages 1042-1055.
    51. Ji, Qiang & Bouri, Elie & Lau, Chi Keung Marco & Roubaud, David, 2019. "Dynamic connectedness and integration in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 257-272.
    52. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    53. Wang, Gang-Jin & Xie, Chi & Wen, Danyan & Zhao, Longfeng, 2019. "When Bitcoin meets economic policy uncertainty (EPU): Measuring risk spillover effect from EPU to Bitcoin," Finance Research Letters, Elsevier, vol. 31(C).
    54. Kalyvas, Antonios & Papakyriakou, Panayiotis & Sakkas, Athanasios & Urquhart, Andrew, 2020. "What drives Bitcoin’s price crash risk?," Economics Letters, Elsevier, vol. 191(C).
    55. Hiran H. Lathabai & Susan George & Thara Prabhakaran & Manoj Changat, 2018. "An integrated approach to path analysis for weighted citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1871-1904, December.
    56. Urom, Christian & Abid, Ilyes & Guesmi, Khaled & Chevallier, Julien, 2020. "Quantile spillovers and dependence between Bitcoin, equities and strategic commodities," Economic Modelling, Elsevier, vol. 93(C), pages 230-258.
    57. John S. Liu & Chung-Huei Kuan, 2016. "A new approach for main path analysis: Decay in knowledge diffusion," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(2), pages 465-476, February.
    58. Xiaorui Jiang & Xiaoping Sun & Zhe Yang & Hai Zhuge & Jianmin Yao, 2016. "Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(7), pages 1679-1702, July.
    59. Abramo, Giovanni, 2018. "Revisiting the scientometric conceptualization of impact and its measurement," Journal of Informetrics, Elsevier, vol. 12(3), pages 590-597.
    60. Phillip, Andrew & Chan, Jennifer S.K. & Peiris, Shelton, 2018. "A new look at Cryptocurrencies," Economics Letters, Elsevier, vol. 163(C), pages 6-9.
    61. Yanan Wang & An Zeng & Ying Fan & Zengru Di, 2019. "Ranking scientific publications considering the aging characteristics of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 155-166, July.
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