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題名 事件導向動態社會網路分析應用於政治權力變化之觀察
An application of event-based dynamic social network analysis for observing political power evolution作者 莊婉君
Chuang, Wan Chun貢獻者 劉吉軒
Liu, Jyi Shane
莊婉君
Chuang, Wan Chun關鍵詞 動態社會網路分析
網路分群
政府專業團隊
政治權力觀察
dynamic social network
community detection
political community
political power observation日期 2011 上傳時間 30-Oct-2012 11:07:48 (UTC+8) 摘要 如何從大量的資料中擷取隱匿或不容易直接觀察的資訊，是重要的議題，社會網路提供了一個適當的系統描述模型與內部檢視分析的方法，過去社會網路分析多著重於靜態的分析，無法解釋發生在網路上的動態行為；我們的研究目的是從動態社會網路分析的角度，觀察政治權力的變化，將資料依時間切分成多個資料集，在各個資料集中，利用官員共同異動職務及共事資料建構網路，並使用EdgeBetweenness分群方法將網路做分群，以找出潛在的政治群組，接著再採用事件導向的方法(Event-based Framework)，比較連續兩個時間區間的網路分群結果，以觀察政治群體的動態發展，找出政治群組事件，並將其匯集成政治群組指標，以用來衡量政治群組的變動性及穩定性。我們提供了一個觀察政治權力變化的模型，透過網路建立、網路分群到觀察網路動態行為，找到不容易直接取得的資訊，我們也以此觀察模型解決以下問題：(1)觀察部門之接班梯隊之變化，(2)觀察特定核心人物之核心成員組成模式，(3)部門專業才能單一性或多元性之觀察。實驗結果顯示，利用政治群組事件設計的政治群組指標，可實際反應政府部門選用人才的傾向為內部調任或外部選用。
Extracting implicit information from a considerable amount of data is an important intelligent data processing task. Social network analysis is appropriate for this purpose due to its emphasis on the relationship between nodes and the structure of networked interactions. Most research in the past has focused on a static point of view. It can`t account for whatever action is taking place in the network. Our research objective is to observe the evolution of political power by dynamic social network analysis. We begin by creating static graphs at different time according to the synchronous job change between the government officials or the relationship between the government officials whom work in the same government agency. We obtain political communities from each of these snapshot graphs using edge betweenness clustering method. Next we define a set of evolutionary events of political communities using event-based framework. We compare two consecutive snapshots to capture the evolutionary events of political communities. We also develop two evolutionary political community metrics to measure the stability of political communities. We propose a model of observing the evolution of political power by three steps－network construction, community identification and community evolution tracking. The approach is shown to be effectual for the purposes of: (1) finding succession pool members in government agencies, (2) observing the inner circle of a leading political figure, (3) measuring the specialized degree of government agencies. Experiments also show that our community evolution metrics reflect the tendency of whether a government agency conducts internal succession or outside appointment.參考文獻 [1] 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.[2] 銓敍部-退撫司, "人事制度研究改進專案小組研究報告-政務人員與常務人員退職年資應否區分之研究," pp. 56-66, 2009.[3] S. Asur, S. Parthasarathy, and D. Ucar, "An event-based framework for characterizing the evolutionary behavior of interaction graphs," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 3, pp. 1-36, 2009.[4] T. Y. Berger-Wolf and J. Saia, "A framework for analysis of dynamic social networks," Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 523-528, August 20-23 2006.[5] P. Bródka, S. Saganowski, and P. Kazienko, "Group Evolution Discovery in Social Networks," International Conference on Advances in Social Networks Analysis and Mining, pp. 247-253, 2011.[6] G. W. Flake, S. Lawrence, C. L. Giles, and F. M. Coetzee, "Self-Organization and Identification of Web Communities," IEEE Computer, vol. 35, pp. 66-71, 2002.[7] L. C. Freeman, "Centrality in social networks conceptual clarification," Social Networks, vol. 1, pp. 215-239, 1978.[8] L. C. Freeman, "Finding social groups: A meta-analysis of the southern women data," Dynamic Social Network Modeling and Analysis. The National Academies, pp. 39-97, 2003.[9] L. C. Freeman, The Development of Social Network Analysis: A Study in the Sociology of Science: BookSurge Publishing, 2004.[10] L. Getoor and C. P. Diehl, "Link mining: a survey," SIGKDD Explor. Newsl., vol. 7, pp. 3-12, 2005.[11] M. Girvan and M. E. J. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, vol. 99, pp. 7821-7826, 2002.[12] J. Hopcroft, O. Khan, B. Kulis, and B. Selman, "Natural communities in large linked networks," Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 541-546, Washington, D.C., 2003.[13] J. Hopcroft, O. Khan, B. Kulis, and B. Selman, "Tracking evolving communities in large linked networks," Proceedings of the National Academy of Sciences of the United States of America, vol. 101, pp. 5249-5253, 2004.[14] D. Kempe, J. Kleinberg, and E. v. Tardos, "Maximizing the spread of influence through a social network," Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137-146, Washington, D.C., 2003.[15] D. Liben-Nowell and J. Kleinberg, "The link-prediction problem for social networks," Journal of the American Society for Information Science and Technology, vol. 58, pp. 1019-1031, 2007.[16] J. S. Liu and K. C. Ning, "Applying link prediction to ranking candidates for high-level government post," In Proceedings of the IEEE/ACM 2011 International Conference on Advances in Social Networks Analysis and Mining, Kaohsiung,Taiwan, pp. 145-152, 2011.[17] J. S. Liu, K. C. Ning, and W. C. Chuang, "Evolutionary Community Detection for Observing Covert Political Elite Cliques," In Proceedings of the IEEE/ACM 2012 International Conference on Advances in Social Networks Analysis and Mining, Istanbul,Turkey, 2012.[18] L. A. Meyers, M. E. J. Newman, and B. Pourbohloul, "Predicting epidemics on directed contact networks," Journal of Theoretical Biology, vol. 240, pp. 400-418, 2006.[19] M. E. J. Newman, "Detecting community structure in networks," The European Physical Journal B - Condensed Matter and Complex Systems, vol. 38, pp. 321-330, 2004.[20] M. E. J. Newman, "Power laws, Pareto distributions and Zipf`s law," Contemporary Physics, vol. 46, pp. 323-351, 2005.[21] M. E. J. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical Review E, vol. 69,026113, 2004.[22] J. O`Madadhain, D. Fisher, and T. Nelson, "JUNG:Java Universal Network/Graph Framework.http://jung.sourceforge.net."[23] G. Palla, A.-L. Barabasi, and T. Vicsek, "Quantifying social group evolution," Nature, vol. 446, pp. 664-667, 2007.[24] E. M. Rogers, Diffusion of Innovations: Simon &Shuster, Inc., 2003.[25] F. Santo, "Community detection in graphs," Physics Reports, vol. 486, pp. 75-174, 2010.[26] S. Sebastian, "A structured overview of 50 years of small-world research," Social Networks, vol. 31, pp. 165-178, 2009.[27] M. Spiliopoulou, "Evolution in Social Networks: A Survey Social Network Data Analytics," C. C. Aggarwal, Ed., ed: Springer US, 2011, pp. 149-175.[28] S. Sundaresan, I. Fischhoff, J. Dushoff, and D. Rubenstein, "Network metrics reveal differences in social organization between two fission–fusion species, Grevy’s zebra and onager," Oecologia, vol. 151, pp. 140-149, 2007.[29] M. Takaffoli, F. Sangi, J. Fagnan, and O. R. Zäıane, "A Framework for Analyzing Dynamic Social Networks," In 7th Conference on Applications of Social Network Analysis (ASNA), 2010.[30] M. Takaffoli, F. Sangi, J. Fagnan, and O. R. Zäıane, "Community Evolution Mining in Dynamic Social Networks," Procedia - Social and Behavioral Sciences, vol. 22, pp. 49-58, 2011.[31] C. Tantipathananandh, T. Berger-Wolf, and D. Kempe, "A framework for community identification in dynamic social networks," Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 717-726, 2007.[32] J. Travers and S. Milgram, "An experimental study of the small world problem," Sociometry, vol. 32, pp. 425-443, 1969.[33] S. Wasserman and K. Faust, Social Network Analysis Methods and Applications. New York : USA, 1994.[34] D. J. Watts, Six Degrees: The Science of a Connected Age 2004.[35] M. Zhang, "Social Network Analysis: History, Concepts, and Research " in Handbook of Social Network Technologies and Applications, B. Furht, Ed., ed: Springer US, 2010, pp. 3-21. 描述 碩士
國立政治大學
資訊科學學系
98971003
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098971003 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) 莊婉君 zh_TW dc.contributor.author (Authors) Chuang, Wan Chun en_US dc.creator (作者) 莊婉君 zh_TW dc.creator (作者) Chuang, Wan Chun en_US dc.date (日期) 2011 en_US dc.date.accessioned 30-Oct-2012 11:07:48 (UTC+8) - dc.date.available 30-Oct-2012 11:07:48 (UTC+8) - dc.date.issued (上傳時間) 30-Oct-2012 11:07:48 (UTC+8) - dc.identifier (Other Identifiers) G0098971003 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54462 - 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) 98971003 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 如何從大量的資料中擷取隱匿或不容易直接觀察的資訊，是重要的議題，社會網路提供了一個適當的系統描述模型與內部檢視分析的方法，過去社會網路分析多著重於靜態的分析，無法解釋發生在網路上的動態行為；我們的研究目的是從動態社會網路分析的角度，觀察政治權力的變化，將資料依時間切分成多個資料集，在各個資料集中，利用官員共同異動職務及共事資料建構網路，並使用EdgeBetweenness分群方法將網路做分群，以找出潛在的政治群組，接著再採用事件導向的方法(Event-based Framework)，比較連續兩個時間區間的網路分群結果，以觀察政治群體的動態發展，找出政治群組事件，並將其匯集成政治群組指標，以用來衡量政治群組的變動性及穩定性。我們提供了一個觀察政治權力變化的模型，透過網路建立、網路分群到觀察網路動態行為，找到不容易直接取得的資訊，我們也以此觀察模型解決以下問題：(1)觀察部門之接班梯隊之變化，(2)觀察特定核心人物之核心成員組成模式，(3)部門專業才能單一性或多元性之觀察。實驗結果顯示，利用政治群組事件設計的政治群組指標，可實際反應政府部門選用人才的傾向為內部調任或外部選用。 zh_TW dc.description.abstract (摘要) Extracting implicit information from a considerable amount of data is an important intelligent data processing task. Social network analysis is appropriate for this purpose due to its emphasis on the relationship between nodes and the structure of networked interactions. Most research in the past has focused on a static point of view. It can`t account for whatever action is taking place in the network. Our research objective is to observe the evolution of political power by dynamic social network analysis. We begin by creating static graphs at different time according to the synchronous job change between the government officials or the relationship between the government officials whom work in the same government agency. We obtain political communities from each of these snapshot graphs using edge betweenness clustering method. Next we define a set of evolutionary events of political communities using event-based framework. We compare two consecutive snapshots to capture the evolutionary events of political communities. We also develop two evolutionary political community metrics to measure the stability of political communities. We propose a model of observing the evolution of political power by three steps－network construction, community identification and community evolution tracking. The approach is shown to be effectual for the purposes of: (1) finding succession pool members in government agencies, (2) observing the inner circle of a leading political figure, (3) measuring the specialized degree of government agencies. Experiments also show that our community evolution metrics reflect the tendency of whether a government agency conducts internal succession or outside appointment. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究目的 11.3 研究資料 21.3.1 總統府公報 21.3.2 政府官員異動資料庫 31.3.3 中央選舉委員會選舉資料庫 41.4 研究貢獻 41.5 論文架構 5第二章 文獻探討 72.1 社會網路分析的歷史 72.2 社會網路分析的模型 92.3 網路分群 112.3.1 群組的定義 112.3.2 分群演算法 142.4 動態社會網路分析 162.4.1 基本概念 162.4.2 Event-Based Framework for Detection of Social Network Evolution 182.5 小結 20第三章 研究方法及系統架構 213.1 政治群組事件定義 243.2 政治群組指標設計 293.3 系統架構 363.3.1 Data Preprocessing 373.3.2 Network Construction 393.3.3 Community Identification 413.3.4 Community Evolution Tracking 43第四章 政府專業團隊動態觀察 474.1 實驗資料 484.2 部門之政府專業團隊 514.2.1 共同異動網路實驗結果說明 514.2.2 共事網路實驗結果說明 634.2.3 結果分析與討論 664.3 核心人物專業團隊觀察 674.3.1 共同異動網路實驗結果說明 674.3.2 共事網路實驗結果說明 704.3.3 結果分析與討論 77第五章 政府部門專業才能單一性或多元性之觀察 795.1 實驗結果說明 795.1.1 操作職等12-15實驗結果說明 805.1.2 操作職等10-15實驗結果說明 875.2 結果分析與討論 885.2.1 合理的時間區間選擇 885.2.2 不同部門之專業才能單一性或多元性之比較 895.2.3 不同操作職等之專業才能單一性或多元性之比較 91第六章 結論與未來研究方向 936.1 結論 936.2 未來研究方向 96參考文獻 97附錄 100附錄A Evolution Event次數統計表(操作職等12-15) 100附錄B Evolution Event次數統計表(操作職等10-15) 102 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098971003 en_US dc.subject (關鍵詞) 動態社會網路分析 zh_TW dc.subject (關鍵詞) 網路分群 zh_TW dc.subject (關鍵詞) 政府專業團隊 zh_TW dc.subject (關鍵詞) 政治權力觀察 zh_TW dc.subject (關鍵詞) dynamic social network en_US dc.subject (關鍵詞) community detection en_US dc.subject (關鍵詞) political community en_US dc.subject (關鍵詞) political power observation en_US dc.title (題名) 事件導向動態社會網路分析應用於政治權力變化之觀察 zh_TW dc.title (題名) An application of event-based dynamic social network analysis for observing political power evolution en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] 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.[2] 銓敍部-退撫司, "人事制度研究改進專案小組研究報告-政務人員與常務人員退職年資應否區分之研究," pp. 56-66, 2009.[3] S. Asur, S. Parthasarathy, and D. Ucar, "An event-based framework for characterizing the evolutionary behavior of interaction graphs," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 3, pp. 1-36, 2009.[4] T. Y. Berger-Wolf and J. Saia, "A framework for analysis of dynamic social networks," Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 523-528, August 20-23 2006.[5] P. Bródka, S. Saganowski, and P. Kazienko, "Group Evolution Discovery in Social Networks," International Conference on Advances in Social Networks Analysis and Mining, pp. 247-253, 2011.[6] G. W. Flake, S. Lawrence, C. L. Giles, and F. M. Coetzee, "Self-Organization and Identification of Web Communities," IEEE Computer, vol. 35, pp. 66-71, 2002.[7] L. C. Freeman, "Centrality in social networks conceptual clarification," Social Networks, vol. 1, pp. 215-239, 1978.[8] L. C. Freeman, "Finding social groups: A meta-analysis of the southern women data," Dynamic Social Network Modeling and Analysis. The National Academies, pp. 39-97, 2003.[9] L. C. Freeman, The Development of Social Network Analysis: A Study in the Sociology of Science: BookSurge Publishing, 2004.[10] L. Getoor and C. P. Diehl, "Link mining: a survey," SIGKDD Explor. Newsl., vol. 7, pp. 3-12, 2005.[11] M. Girvan and M. E. J. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, vol. 99, pp. 7821-7826, 2002.[12] J. Hopcroft, O. Khan, B. Kulis, and B. Selman, "Natural communities in large linked networks," Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 541-546, Washington, D.C., 2003.[13] J. Hopcroft, O. Khan, B. Kulis, and B. Selman, "Tracking evolving communities in large linked networks," Proceedings of the National Academy of Sciences of the United States of America, vol. 101, pp. 5249-5253, 2004.[14] D. Kempe, J. Kleinberg, and E. v. Tardos, "Maximizing the spread of influence through a social network," Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137-146, Washington, D.C., 2003.[15] D. Liben-Nowell and J. Kleinberg, "The link-prediction problem for social networks," Journal of the American Society for Information Science and Technology, vol. 58, pp. 1019-1031, 2007.[16] J. S. Liu and K. C. Ning, "Applying link prediction to ranking candidates for high-level government post," In Proceedings of the IEEE/ACM 2011 International Conference on Advances in Social Networks Analysis and Mining, Kaohsiung,Taiwan, pp. 145-152, 2011.[17] J. S. Liu, K. C. Ning, and W. C. Chuang, "Evolutionary Community Detection for Observing Covert Political Elite Cliques," In Proceedings of the IEEE/ACM 2012 International Conference on Advances in Social Networks Analysis and Mining, Istanbul,Turkey, 2012.[18] L. A. Meyers, M. E. J. Newman, and B. Pourbohloul, "Predicting epidemics on directed contact networks," Journal of Theoretical Biology, vol. 240, pp. 400-418, 2006.[19] M. E. J. Newman, "Detecting community structure in networks," The European Physical Journal B - Condensed Matter and Complex Systems, vol. 38, pp. 321-330, 2004.[20] M. E. J. Newman, "Power laws, Pareto distributions and Zipf`s law," Contemporary Physics, vol. 46, pp. 323-351, 2005.[21] M. E. J. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical Review E, vol. 69,026113, 2004.[22] J. O`Madadhain, D. Fisher, and T. Nelson, "JUNG:Java Universal Network/Graph Framework.http://jung.sourceforge.net."[23] G. Palla, A.-L. Barabasi, and T. Vicsek, "Quantifying social group evolution," Nature, vol. 446, pp. 664-667, 2007.[24] E. M. Rogers, Diffusion of Innovations: Simon &Shuster, Inc., 2003.[25] F. Santo, "Community detection in graphs," Physics Reports, vol. 486, pp. 75-174, 2010.[26] S. Sebastian, "A structured overview of 50 years of small-world research," Social Networks, vol. 31, pp. 165-178, 2009.[27] M. Spiliopoulou, "Evolution in Social Networks: A Survey Social Network Data Analytics," C. C. Aggarwal, Ed., ed: Springer US, 2011, pp. 149-175.[28] S. Sundaresan, I. Fischhoff, J. Dushoff, and D. Rubenstein, "Network metrics reveal differences in social organization between two fission–fusion species, Grevy’s zebra and onager," Oecologia, vol. 151, pp. 140-149, 2007.[29] M. Takaffoli, F. Sangi, J. Fagnan, and O. R. Zäıane, "A Framework for Analyzing Dynamic Social Networks," In 7th Conference on Applications of Social Network Analysis (ASNA), 2010.[30] M. Takaffoli, F. Sangi, J. Fagnan, and O. R. Zäıane, "Community Evolution Mining in Dynamic Social Networks," Procedia - Social and Behavioral Sciences, vol. 22, pp. 49-58, 2011.[31] C. Tantipathananandh, T. Berger-Wolf, and D. Kempe, "A framework for community identification in dynamic social networks," Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 717-726, 2007.[32] J. Travers and S. Milgram, "An experimental study of the small world problem," Sociometry, vol. 32, pp. 425-443, 1969.[33] S. Wasserman and K. Faust, Social Network Analysis Methods and Applications. New York : USA, 1994.[34] D. J. Watts, Six Degrees: The Science of a Connected Age 2004.[35] M. Zhang, "Social Network Analysis: History, Concepts, and Research " in Handbook of Social Network Technologies and Applications, B. Furht, Ed., ed: Springer US, 2010, pp. 3-21. zh_TW
