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  • × language_ss:"e"
  • × year_i:[2020 TO 2030}
  1. Zhu, Y.; Quan, L.; Chen, P.-Y.; Kim, M.C.; Che, C.: Predicting coauthorship using bibliographic network embedding (2023) 0.06 
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    Abstract
    Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph-based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real-world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation-based methods were applied to the entities and relationships to generate their low-dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation-based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework.
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.04 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  3. Fugmann, R.: What is information? : an information veteran looks back (2022) 0.02 
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    Date
    18. 8.2022 19:22:57
  4. Morris, V.: Automated language identification of bibliographic resources (2020) 0.01 
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    Date
    2. 3.2020 19:04:22
  5. Tay, A.: ¬The next generation discovery citation indexes : a review of the landscape in 2020 (2020) 0.01 
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    Date
    17.11.2020 12:22:59
  6. Manley, S.: Letters to the editor and the race for publication metrics (2022) 0.01 
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    Date
    6. 4.2022 19:22:26
  7. Wu, P.F.: Veni, vidi, vici? : On the rise of scrape-and-report scholarship in online reviews research (2023) 0.01 
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    Date
    22. 1.2023 18:33:53
  8. Candela, G.: ¬An automatic data quality approach to assess semantic data from cultural heritage institutions (2023) 0.01 
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    Date
    22. 6.2023 18:23:31
  9. Geras, A.; Siudem, G.; Gagolewski, M.: Should we introduce a dislike button for academic articles? (2020) 0.01 
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    Date
    6. 1.2020 18:10:22
  10. Bullard, J.; Dierking, A.; Grundner, A.: Centring LGBT2QIA+ subjects in knowledge organization systems (2020) 0.01 
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    Date
    6.10.2020 21:22:33
  11. Lorentzen, D.G.: Bridging polarised Twitter discussions : the interactions of the users in the middle (2021) 0.01 
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    Date
    20. 1.2015 18:30:22
  12. Park, Y.J.: ¬A socio-technological model of search information divide in US cities (2021) 0.01 
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    20. 1.2015 18:30:22
  13. Cooke, N.A.; Kitzie, V.L.: Outsiders-within-Library and Information Science : reprioritizing the marginalized in critical sociocultural work (2021) 0.01 
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    Date
    18. 9.2021 13:22:27
  14. Zheng, X.; Chen, J.; Yan, E.; Ni, C.: Gender and country biases in Wikipedia citations to scholarly publications (2023) 0.01 
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    Date
    22. 1.2023 18:53:32
  15. Ma, Y.: Relatedness and compatibility : the concept of privacy in Mandarin Chinese and American English corpora (2023) 0.01 
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    22. 1.2023 18:59:40
  16. Ma, L.: Information, platformized (2023) 0.01 
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    Date
    22. 1.2023 19:01:47
  17. Hertzum, M.: Information seeking by experimentation : trying something out to discover what happens (2023) 0.01 
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    Date
    21. 3.2023 19:22:29
  18. Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023) 0.01 
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    21. 3.2023 19:22:14
  19. Kuehn, E.F.: ¬The information ecosystem concept in information literacy : a theoretical approach and definition (2023) 0.01 
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    22. 3.2023 11:52:50
  20. Li, G.; Siddharth, L.; Luo, J.: Embedding knowledge graph of patent metadata to measure knowledge proximity (2023) 0.01 
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    Date
    22. 3.2023 12:06:55

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