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題名 以多觀點社群網路模型應用於政府官職繼任評選之探討
An Investigation on the Application of Multiperspective Social Network Model for Government Post Succession Evaluation作者 林專耀
Lin, Zhuan Yao貢獻者 劉吉軒
Liu, Jyi Shane
林專耀
Lin, Zhuan Yao關鍵詞 社群網路分析
多觀點社群網路模型
連結預測
政府官職繼任評選
Social Network Analysis
Multiperspective Social Network Model
Link Prediction
Government Post Succession Evaluation日期 2013 上傳時間 1-Oct-2013 13:47:48 (UTC+8) 摘要 隨著個人電腦與網際網路科技的逐漸成熟，網路上每日都有巨量資料（Big Data）產生。近年來隨著社群網站的崛起，如何處理這些巨量的社群資料，並有效率地提供出有意義的社群資訊，將是這幾年社群網路領域研究的重點。每當內閣改組消息一出的時候，各政府部門單位的官職繼任官員，都會成為社會公眾關注的議題。本研究將使用中華民國政府官職資料庫，以社群網路分析與連結預測理論為基礎，並透過資料庫中所提供的資料，隨著不同評選時間點以及評選官職建置出網路。擷取網路的資訊，利用本文所提出的多面向模型（Multiperspective Model）產生多種觀點的分數。接著使用評選模型（Evaluation Model）將各個觀點的分數整合，進行某官員繼任某官職可能性計算，然後輸出官職繼任官員的評選清單（Evaluation List）。最後對輸出的評選清單分別對空降繼任狀況、各級上司對於繼任人選決定影響力、單一觀點與多觀點評選方式的評選結果、多觀點評選方式下重視的觀點，以及官職繼任成因五項分析進行探討。
With the well development of personal PC and the Internet technology, there is a huge amount of data (Big Data) being generated on the Internet every day. Because of the debut and rise of social websites, how to deal with such a huge amount of community information as well as efficiently provide meaningful data to the public has been an explored main issue in the field of social network research in recent years. When the news about cabinet changing was released, the successor of various government departments will become the actively concerned topic for the public. This research applied a government position transaction database as the elements to build the network, which based on Social Network Analysis and Link Prediction theory with different evaluation position and evaluation time. Captured information in the network was used to generate the scores of multiple perspectives according to the Multiperspective Model. Then using the Evaluation Model, which can integrate each observed perspective, and calculate the probability of an official succeeds of a position. Finally the network could output the evaluation list of position successor. In the end, the outcome of the evaluation list was applied to analyze and discuss the following 5 research questions: The situation that the successor isn’t from the unit of successive position, the influence of all levels superiors on the succession decision, result of evaluative methods of a single view and multiple perspective, the important perspective of Multiperspective evaluation, and causal relationship of official successor.參考文獻 沈曜廷, 應用社會網路連結預測理論於政府官員職務繼任分析. 國立政治大學資訊科學系碩士論文, 2012.林岡隆, 政府官員異動之社會網路分析. 國立政治大學資訊科學系碩士論文, 2009.林嘉誠, 政務首長的流動分析2000.5-2007.5. 第三卷第四期, 國家菁英, 2006, 頁1–28.胡龍騰, 政黨輪替前後高階行政主官流動之比較. 第三卷第四期, 國家菁英, 2007, 頁31–42.黃俊生, 基於社會網路分析連結預測理論之政府官員職位與職務歷程影響研究. 國立政治大學資訊科學系碩士論文, 2010.劉吉軒, 中華民國政府官職資料庫發展與應用. 第四卷第二期, 圖書與資訊學刊, 2012, 頁1-32.顏秋來, 政務官與事務官體制運作之研究. 第二卷第一期, 國家菁英, 2006, 頁21–28.A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel. You are who you know: inferring user profiles in online social networks. Proceedings of the third ACM international conference on Web search and data mining, 2010.A. Popescul, and L. H. Ungar. Statistical relational learning for link prediction. IJCAI workshop on learning statistical models from relational data. Vol. 2003, 2003.D. Liben-Nowell, and J. Kleinberg. The link‐prediction problem for social networks. Journal of the American society for information science and technology, Vol. 8, No. 7, 2007, pp. 1019-1031.D. Lin. An information-theoretic definition of similarity. Proceedings of the 15th international conference on Machine Learning. Vol. 1, 1998.J. M. Montoya, and R. V. Solé. Small world patterns in food webs. Journal of Theoretical Biology, Vol. 214, No.3-7, 2002, pp. 405-412.J. O`Madadhain, J. Hutchins, and P. Smyth. Prediction and ranking algorithms for event-based network data. ACM SIGKDD Explorations Newsletter, Vol. 7, No.2, 2005, pp. 23-30.K. Moutselos, I. Maglogiannis, and A. Chatziioannou. GOrevenge: a novel generic reverse engineering method for the identification of critical molecular players, through the use of ontologies. Biomedical Engineering, IEEE Transactions on, Vol.58, No.12, 2011, pp. 3522-3527.L. Getoor, and C. P. Diehl. Link mining: a survey. ACM SIGKDD Explorations Newsletter, Vol.7, No.2, 2005, pp. 3-12.L. Lü, and T. Zhou. Link prediction in weighted networks: The role of weak ties. EPL (Europhysics Letters), Vol.89, No.1, 2010, pp. 18001.L. Lü, C. H. Jin, and T. Zhou. Similarity index based on local paths for link prediction of complex networks. Physical Review, Vol. 80, No. 4, 2009, pp. 46122.M. Al Hasan, V. Chaoji, S. Salem, and M. Zaki. Link prediction using supervised learning. SDM’06: Workshop on Link Analysis, Counter-terrorism and Security. 2006.M. Freire, C. Plaisant, B. Shneiderman, and J. Golbeck. ManyNets: an interface for multiple network analysis and visualization. Proceedings of the 28th ACM international conference on Human factors in computing systems, 2010, pp. 213-222.R. Albert, H. Jeong, and A. L. Barabasi. Internet: Diameter of the world-wide web, Nature, Vol. 401, No. 6749, 1999, pp. 130.R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. Link Mining: Models, Algorithms, and Applications. Springer New York, 2010, pp. 337-357.S. Wasserman. Social network analysis: Methods and applications. Cambridge university press, Vol. 8, 1994.T. Murata, and S. Moriyasu. Link prediction of social networks based on weighted proximity measures. Web Intelligence, IEEE/WIC/ACM International Conference on. IEEE, 2007.Z. Yang, D. Fu, Y. Tang, Y. Zhang, Y. Hao, C. Gui, X. Ji, and X. Yue. Link prediction Based on Weighted Networks. AsiaSim 2012. Springer Berlin Heidelberg, 2012, pp. 119-126. 描述 碩士
國立政治大學
資訊科學學系
100753040
102資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100753040 資料類型 thesis dc.contributor.advisor 劉吉軒 zh_TW dc.contributor.advisor Liu, Jyi Shane en_US dc.contributor.author (Authors) 林專耀 zh_TW dc.contributor.author (Authors) Lin, Zhuan Yao en_US dc.creator (作者) 林專耀 zh_TW dc.creator (作者) Lin, Zhuan Yao en_US dc.date (日期) 2013 en_US dc.date.accessioned 1-Oct-2013 13:47:48 (UTC+8) - dc.date.available 1-Oct-2013 13:47:48 (UTC+8) - dc.date.issued (上傳時間) 1-Oct-2013 13:47:48 (UTC+8) - dc.identifier (Other Identifiers) G0100753040 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/61203 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 100753040 zh_TW dc.description (描述) 102 zh_TW dc.description.abstract (摘要) 隨著個人電腦與網際網路科技的逐漸成熟，網路上每日都有巨量資料（Big Data）產生。近年來隨著社群網站的崛起，如何處理這些巨量的社群資料，並有效率地提供出有意義的社群資訊，將是這幾年社群網路領域研究的重點。每當內閣改組消息一出的時候，各政府部門單位的官職繼任官員，都會成為社會公眾關注的議題。本研究將使用中華民國政府官職資料庫，以社群網路分析與連結預測理論為基礎，並透過資料庫中所提供的資料，隨著不同評選時間點以及評選官職建置出網路。擷取網路的資訊，利用本文所提出的多面向模型（Multiperspective Model）產生多種觀點的分數。接著使用評選模型（Evaluation Model）將各個觀點的分數整合，進行某官員繼任某官職可能性計算，然後輸出官職繼任官員的評選清單（Evaluation List）。最後對輸出的評選清單分別對空降繼任狀況、各級上司對於繼任人選決定影響力、單一觀點與多觀點評選方式的評選結果、多觀點評選方式下重視的觀點，以及官職繼任成因五項分析進行探討。 zh_TW dc.description.abstract (摘要) With the well development of personal PC and the Internet technology, there is a huge amount of data (Big Data) being generated on the Internet every day. Because of the debut and rise of social websites, how to deal with such a huge amount of community information as well as efficiently provide meaningful data to the public has been an explored main issue in the field of social network research in recent years. When the news about cabinet changing was released, the successor of various government departments will become the actively concerned topic for the public. This research applied a government position transaction database as the elements to build the network, which based on Social Network Analysis and Link Prediction theory with different evaluation position and evaluation time. Captured information in the network was used to generate the scores of multiple perspectives according to the Multiperspective Model. Then using the Evaluation Model, which can integrate each observed perspective, and calculate the probability of an official succeeds of a position. Finally the network could output the evaluation list of position successor. In the end, the outcome of the evaluation list was applied to analyze and discuss the following 5 research questions: The situation that the successor isn’t from the unit of successive position, the influence of all levels superiors on the succession decision, result of evaluative methods of a single view and multiple perspective, the important perspective of Multiperspective evaluation, and causal relationship of official successor. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究資料 21.2.1 政府公報 21.2.2 中華民國政府官職資料庫 41.2.3 上級機關與下級機關資料 61.3 研究動機與目的 61.4 研究貢獻 71.5 論文架構 9第二章 文獻探討 102.1 社群網路分析 102.2 連結預測理論 162.2.1方法 162.2.2 相關研究 192.3 總結 19第三章 政府官職繼任官員評選系統架構與模型 203.1 政府官職繼任官員評選系統 203.2 資料前置處理 223.3 網路模型 243.3.1 Subordinate-and-Superior Multilevel Weighted Network 253.3.2 Candidate-and-Position Multilevel Weighted Network 263.3.3 Official-and-Position Weighted Network 273.3.4 小結 293.4 多面向模型 293.4.1 共事分數 303.4.2 年資分數 313.4.3 官職相配分數 333.4.4 標準化 343.5 評選模型 36第四章 實驗設計與分析探討 384.1 實驗資料 384.2 實驗設計 414.3 實驗分析 434.3.1 各部門單位空降狀況分析 434.3.2 各級上司對官職繼任人選決定權影響力分析 484.3.3 各部門單一觀點與多觀點繼任評選結果分析 514.3.4 評選模型權重參數討論於各部門 524.3.5 各部門單位官職之繼任成因分析 554.3.5.1 原能會 724.3.5.2 經濟部 744.3.5.3 財政部 764.3.5.4 小結 794.4 實驗總結 86第五章 結論與未來研究方向 895.1結論 895.2未來研究方向 90參考文獻 92 zh_TW dc.format.extent 6307544 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100753040 en_US dc.subject (關鍵詞) 社群網路分析 zh_TW dc.subject (關鍵詞) 多觀點社群網路模型 zh_TW dc.subject (關鍵詞) 連結預測 zh_TW dc.subject (關鍵詞) 政府官職繼任評選 zh_TW dc.subject (關鍵詞) Social Network Analysis en_US dc.subject (關鍵詞) Multiperspective Social Network Model en_US dc.subject (關鍵詞) Link Prediction en_US dc.subject (關鍵詞) Government Post Succession Evaluation en_US dc.title (題名) 以多觀點社群網路模型應用於政府官職繼任評選之探討 zh_TW dc.title (題名) An Investigation on the Application of Multiperspective Social Network Model for Government Post Succession Evaluation en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 沈曜廷, 應用社會網路連結預測理論於政府官員職務繼任分析. 國立政治大學資訊科學系碩士論文, 2012.林岡隆, 政府官員異動之社會網路分析. 國立政治大學資訊科學系碩士論文, 2009.林嘉誠, 政務首長的流動分析2000.5-2007.5. 第三卷第四期, 國家菁英, 2006, 頁1–28.胡龍騰, 政黨輪替前後高階行政主官流動之比較. 第三卷第四期, 國家菁英, 2007, 頁31–42.黃俊生, 基於社會網路分析連結預測理論之政府官員職位與職務歷程影響研究. 國立政治大學資訊科學系碩士論文, 2010.劉吉軒, 中華民國政府官職資料庫發展與應用. 第四卷第二期, 圖書與資訊學刊, 2012, 頁1-32.顏秋來, 政務官與事務官體制運作之研究. 第二卷第一期, 國家菁英, 2006, 頁21–28.A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel. You are who you know: inferring user profiles in online social networks. Proceedings of the third ACM international conference on Web search and data mining, 2010.A. Popescul, and L. H. Ungar. Statistical relational learning for link prediction. IJCAI workshop on learning statistical models from relational data. Vol. 2003, 2003.D. Liben-Nowell, and J. Kleinberg. The link‐prediction problem for social networks. Journal of the American society for information science and technology, Vol. 8, No. 7, 2007, pp. 1019-1031.D. Lin. An information-theoretic definition of similarity. Proceedings of the 15th international conference on Machine Learning. Vol. 1, 1998.J. M. Montoya, and R. V. Solé. Small world patterns in food webs. Journal of Theoretical Biology, Vol. 214, No.3-7, 2002, pp. 405-412.J. O`Madadhain, J. Hutchins, and P. Smyth. Prediction and ranking algorithms for event-based network data. ACM SIGKDD Explorations Newsletter, Vol. 7, No.2, 2005, pp. 23-30.K. Moutselos, I. Maglogiannis, and A. Chatziioannou. GOrevenge: a novel generic reverse engineering method for the identification of critical molecular players, through the use of ontologies. Biomedical Engineering, IEEE Transactions on, Vol.58, No.12, 2011, pp. 3522-3527.L. Getoor, and C. P. Diehl. Link mining: a survey. ACM SIGKDD Explorations Newsletter, Vol.7, No.2, 2005, pp. 3-12.L. Lü, and T. Zhou. Link prediction in weighted networks: The role of weak ties. EPL (Europhysics Letters), Vol.89, No.1, 2010, pp. 18001.L. Lü, C. H. Jin, and T. Zhou. Similarity index based on local paths for link prediction of complex networks. Physical Review, Vol. 80, No. 4, 2009, pp. 46122.M. Al Hasan, V. Chaoji, S. Salem, and M. Zaki. Link prediction using supervised learning. SDM’06: Workshop on Link Analysis, Counter-terrorism and Security. 2006.M. Freire, C. Plaisant, B. Shneiderman, and J. Golbeck. ManyNets: an interface for multiple network analysis and visualization. Proceedings of the 28th ACM international conference on Human factors in computing systems, 2010, pp. 213-222.R. Albert, H. Jeong, and A. L. Barabasi. Internet: Diameter of the world-wide web, Nature, Vol. 401, No. 6749, 1999, pp. 130.R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. Link Mining: Models, Algorithms, and Applications. Springer New York, 2010, pp. 337-357.S. Wasserman. Social network analysis: Methods and applications. Cambridge university press, Vol. 8, 1994.T. Murata, and S. Moriyasu. Link prediction of social networks based on weighted proximity measures. Web Intelligence, IEEE/WIC/ACM International Conference on. IEEE, 2007.Z. Yang, D. Fu, Y. Tang, Y. Zhang, Y. Hao, C. Gui, X. Ji, and X. Yue. Link prediction Based on Weighted Networks. AsiaSim 2012. Springer Berlin Heidelberg, 2012, pp. 119-126. zh_TW
