請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99081完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 潘建興 | zh_TW |
| dc.contributor.advisor | Kin-Hing Phoa | en |
| dc.contributor.author | 賴銘彥 | zh_TW |
| dc.contributor.author | Ming-Yen Lai | en |
| dc.date.accessioned | 2025-08-21T16:18:45Z | - |
| dc.date.available | 2025-08-26 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-06 | - |
| dc.identifier.citation | Abbasi, A., Chung, K.S.K., Hossain, L.: Egocentric analysis of co-authorship network structure, position and performance. Information Processing & Management, 48(4), 671-679 (2012).
A. Velez‑Estevez, P. García‑Sánchez, J. A. Moral‑Munoz, M. J. Cobo: Why do papers from international collaborations get more citations? A bibliometric analysis of Library and Information Science papers. Scientometrics 127:7517–7555 (2022). Matteo Cinelli, Giovanna Ferraro, Antonio Iovanella: Connections matter: a proxy measure for evaluating network membership with an application to the Seventh Research Framework Programme. Scientometrics 127:3959–3976 (2022) Bos, J.: Numerical optimization of the thickness distribution of three-dimensional structures with respect to their structural acoustic properties. Structural and Multidisciplinary Optimization, 32(1), 12-30 (2006). Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of networks with aging of sites. Physical Review E, 62(2), 1842 (2012). Jung, H., Phoa, F.K.H., Ashouri, M. A Leading Author Model for the Popularity Effect on Scientific Collaboration. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1015. Springer, Cham.(2022) Ghiasi, G., Harsh, M., Schiffauerova, A.: Inequality and collaboration patterns in Canadian nanotechnology: implications for pro-poor and gender-inclusive policy. Scientometrics, 115(2), 785-815 (2018). Kuppler, M. Predicting the future impact of Computer Science researchers: Is there a gender bias?. Scientometrics 127, 6695–6732 (2022) Jeong, H., Neda, Z., Barabasi, A-L.: Measuring preferential attachment in evolving networks. Europhysics Letters, 61(4), 567 (2003). Jung, H., Lee, J-G., Kim, S-H.: On the analysis of fitness change: fitness-popularity dynamic network model with varying fitness. Journal of Statistical Mechanics: Theory and Experiment, 2020(4), 043407 (2020). Jung, H., Lee, J-G., Lee, N., Kim, S-H.: PTEM: A popularity-based topical expertise model for community question answering. Annals of Applied Statistics, 14(3), 1304-1325 (2020). Louis, T.A.: Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 44(2), 226-233 (1982). Lu, H., Feng, Y.: A measure of authors' centrality in co-authorship networks based on the distribution of collaborative relationships. Scientometrics, 81(2), 499-511 (2009). Jung, H., Phoa, F.K.H., Ashouri, M.: A Leading Author Model for the Popularity Effect on Scientific Collaboration. Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer(2022) Merton, R.K.: The Matthew effect in science: The reward and communication systems of science are considered. Science, 159(3810), 56-63 (1968). Metz, T., Jackle, S.: Patterns of publishing in political science journals: An overview of our profession using bibliographic data and a co-authorship network. PS, Political Science & Politics, 50(1), 157-165 (2017). Perc, M.: The Matthew effect in empirical data. Journal of The Royal Society Interface, 11(98), 20140178 (2014). Rode, S.M., Pennisi, P.R.C., Beaini, T.L.,Curi, J.P., Cardoso, S.V., Paranhos, L.R.: Authorship, plagiarism, and copyright transfer in the scientific universe. Clinics, 74 , 1312 (2019). Roy, S., Ravindran, B.: Measuring network centrality using hypergraphs. Proceedings of the Second ACM IKDD Conference on Data Sciences, (pp. 59-68) (2015). | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99081 | - |
| dc.description.abstract | 隨著研究主題變得專業化和多元化，政府和組織如何有效分配有限的研究資源已變得至關重要但極具挑戰性。馬太效應是指成功的作者往往會更加成功，而知名度較低的作者則往往難以獲得認可的現象，這種現象在科學合作網絡中被觀察到。在我們之前的研究中，我們在不同假設條件下建立了科學合作網絡模型，並量化了流行效應（也稱為馬太效應[15]）對研究人員和學科的影響，以及研究人員在該學科中的天才程度。在本文中，我們將此模型應用於從科學網（WoS）資料庫收集的不同學科，並根據模型中每個學科的參數對其進行分析。我們使用這些指標創建了一個圖表，並分析了任意兩個學科之間這兩個指標相似性的關係。 | zh_TW |
| dc.description.abstract | As research topics become specialized and diverse, it has become crucial but challenging for governments and organizations to distribute limited research resources effectively. The Matthew effect, which refers to a phenomenon where successful authors tend to be more successful while lesser-known authors tend to struggle to gain recognition, is observed in the scientific collaboration network. In our prior research, we developed a scientific collaboration network model under different hypotheses and quantified the impact of the popular effect (also known as the Matthew effect[15]) on both the researcher and the subject, as well as the researcher's level of genius within the subject. In this paper, we apply this model to different subjects collected from the Web of Science (WoS) database and analyze them based on the parameters of each subject in the model. We used these indicators to create a plot and analyze the relationship between the similarities of the two indicators for any two subjects. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-21T16:18:45Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-21T16:18:45Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書………………………………………………………………. i
誌謝………………………………………………………………………………. ii 中文摘要…………………………………………………………………………. iii Abstract…………………………………………………………………………. iv 目次…………………………………………………………………………………………………v 1. Introduction……………………………………………………………. 1 2. A literature review in the field of scientometrics…… 2 3. The leading author model…………………………………… 4 4. Algorithm…………………………………………………………….. 4 4.1 Preliminary……………………………………………………………… 4 4.2 The likelihood Function…………………………………………………… 5 4.3 Gibbs Sampling…………………………………………………………… 6 4.4 EM Algorithm…………………………………………………………… 8 4.5 Inference…………………………………………………………………… 9 5. Real Data Analytze…………………………………………………… 9 6. Reference…………………………………………………………….. 25 | - |
| dc.language.iso | en | - |
| dc.subject | 相關度 | zh_TW |
| dc.subject | 流行效應 | zh_TW |
| dc.subject | 合作網絡 | zh_TW |
| dc.subject | Web of Science | zh_TW |
| dc.subject | 馬太效應 | zh_TW |
| dc.subject | Web of Science | en |
| dc.subject | Rank of relation | en |
| dc.subject | Coauthorship Network | en |
| dc.subject | Popular effect | en |
| dc.title | 科學合作網絡的綜合研究:基於網絡分析技術 | zh_TW |
| dc.title | A comprehensive study on the scientific collaboration networks via techniques in network analysis | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 楊鈞澔 | zh_TW |
| dc.contributor.coadvisor | JUN-HAO YANG | en |
| dc.contributor.oralexamcommittee | 劉維中;顏佐榕 | zh_TW |
| dc.contributor.oralexamcommittee | Wei-chung Liu;Tso-Jung Yen | en |
| dc.subject.keyword | 馬太效應,Web of Science,合作網絡,相關度,流行效應, | zh_TW |
| dc.subject.keyword | Popular effect,Web of Science,Rank of relation,Coauthorship Network, | en |
| dc.relation.page | 26 | - |
| dc.identifier.doi | 10.6342/NTU202501640 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-06 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資料科學學位學程 | - |
| dc.date.embargo-lift | 2025-08-26 | - |
| 顯示於系所單位： | 資料科學學位學程 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf | 10.29 MB | Adobe PDF | 檢視/開啟 |
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