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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 唐牧群(Muh-Chyun Tang) | |
| dc.contributor.author | Chia-Wei Chen | en |
| dc.contributor.author | 陳家薇 | zh_TW |
| dc.date.accessioned | 2022-11-23T08:59:12Z | - |
| dc.date.available | 2021-11-03 | |
| dc.date.available | 2022-11-23T08:59:12Z | - |
| dc.date.copyright | 2021-11-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-26 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79376 | - |
| dc.description.abstract | 連結預測能透過演算法針對網絡的特定特性來預測未來連結，在資訊科學領域中，若運用在學術合作網絡可以預測未來可能合作的作者，於共字網絡中能預測字詞間可能產生的新連結，用於發掘特定學科領域的變化以及新興的研究領域。目前既有的連結預測表現評估方法為二元分類，但是當網絡的連結具有權重時，二元分類會將檢驗的預測結果轉為二元的形式，忽略連結權重的數值高低，因此本研究提出以網絡統計檢定中的Quadratic Assignment Procedure（QAP）線性迴歸分析作為新的評估典範。 本研究選擇探討領域為生物醫學領域中的三個主題，分別為發育生物學、神經可塑性、端粒，從PubMed資料庫蒐集文獻形成由關鍵字組成的共字網絡，節點為關鍵字，連結是成對關鍵字出現在同一篇文獻的次數。將2015年至2017年形成的共字網絡視為訓練網絡，而2018年2020年的驗證網絡是為了檢驗預測表現的真實網絡。本研究選擇區域演算法中的Adamic/Adar、Common Neighbours、Cosine Similarity以及Jaccard和全域演算法可以設定參數的Katz，針對訓練網絡進行訓練，預測在2018年2020年間可能產生的連結，並使用二元分類和QAP評估預測結果，比較五種演算法的預測表現。 研究結果發現在三個主題中，二元分類與QAP的結果顯示全域演算法的表現優於區域演算法，Katz預測能力最為優異，最後則是Jaccard。雖然整體而言二元分類和QAP的結果並無莫大的差異，但是QAP對於連結權重數值的敏感度高，更精準地將預測結果反應在評估的數值上，此外QAP即使在全為真陽性連結的檢驗中，仍然能判別不同模型於預測連結權重的表現，相較於二元分類，QAP更具有區辨不同模型預測表現的能力。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T08:59:12Z (GMT). No. of bitstreams: 1 U0001-2510202114404200.pdf: 4738795 bytes, checksum: 51414a530439ae6a2dca1888aeda715d (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 論文口試委員審定書 i 誌謝 ii 摘要 iii Abstract iv 圖目次 viii 表目次 x 名詞定義表 xii 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 4 第三節 名詞解釋 5 第二章 文獻回顧 8 第一節 連結預測方法與應用研究 8 第二節 連結預測演算法 12 第三節 連結預測效能評估的新舊模型 16 第四節 連結預測文獻統整 21 第三章 研究設計與實施 23 第一節 共字網絡形成 24 第二節 連結預測效能評估方法 34 第四章 研究結果 38 第一節 訓練網絡與驗證網絡 38 第二節 預測網絡 43 第三節 二元分類評估結果 47 第四節 QAP線性迴歸分析評估結果 54 第五章 結論 71 第一節 綜合比較評估結果 71 第二節 QAP線性迴歸分析的應用與貢獻 75 第三節 研究限制與建議 76 參考文獻 79 附錄 84 | |
| dc.language.iso | zh-TW | |
| dc.title | 建立連結預測的新評估模型—以生物醫學領域的共字網絡為例 | zh_TW |
| dc.title | Toward a New Evaluation Model for Link Prediction: A Case Study of Co-word Network in Biomedical Science | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林頌堅(Hsin-Tsai Liu),吳怡瑾(Chih-Yang Tseng) | |
| dc.subject.keyword | 連結預測,連結預測評估方式,共字網絡,社會網絡分析,網絡迴歸分析,二次指派程序, | zh_TW |
| dc.subject.keyword | Link prediction,Link prediction evaluation,Co-word network,Social network analysis,Regression of network,Quadratic assignment procedure, | en |
| dc.relation.page | 87 | |
| dc.identifier.doi | 10.6342/NTU202104143 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-27 | |
| dc.contributor.author-college | 文學院 | zh_TW |
| dc.contributor.author-dept | 圖書資訊學研究所 | zh_TW |
| 顯示於系所單位： | 圖書資訊學系 | |
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