第六章 結論與未來研究方向
6.2 未來研究方向
國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
6.2 未來研究方向
以下是本研究未來可改進的幾個方向，做為未來研究之參考：
（1） 加入其他維度資料：如加入個人的背景資料，如家世、學歷、年齡、性別、
政黨，以正確補捉官員之間的互動關係，。
（2） 加入其他資料來源：如加入官員在其他單位如政黨、國營企業、大學教授 的任職務紀錄，以完整的涵蓋政治人物完整的職務歷程。
（3） 整合異質資料：如社會事件、新聞正反面意見出現次，並以此設計權重，
以增加分群結果的合理性。
（4） 分群演算法：真實世界中每個人的角色在群體中可能同時存在多個，例如 家族網路中，一個人同時扮演父母或子女等角度，本研究假設每個人只能屬於一個 群組，較不符現實狀況，因此允許同一個人可以跨多個群組更能反應真實，可利用 職等、共事時間長短、職務異動天數差距等資訊，做為網路的權重值，建立 weighted network。
（5） 政治群組事件條件：本研究利用集合內政治個體的相似度的比較(聯集和 交集)，來做為事件判斷的條件，我們採用官員姓名相似度方法，實際上政治權力的 影響力，並不只是特定官員，相同政黨、相同部門的人，也可能形成政治群組，因 此可利用其他維度之資料，做為相似度之條件比較。
（6） 加入政治個體的行為指標，如離開、加入，可利用個體行為設計相關的指 標，如影響力，忠誠度，並利用此指標找出網路的關鍵人物或應用於連結預測(Link Prediction)。
（7） 政治群組指標的計算：在我們的研究之中，政治群組事件圖中，關連的權 重是 Event Ratio，即上一個時期的成員延續到下一個時期的成員比例，實際上操作 職等越高的官員，其影響力越大，因此將政治群組事件加入權重，以區分不同強度，
例如把操作職等和政治個體行為指標做搭配，如越多操作職等高的官員加入或離開 群組，則群組事件的強度比操作職等低的官員加入或離開群組的群組事件高。
‧
[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,
‧
[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.
‧
‧
‧
‧
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Units Dissolve Form Split Merge Continue Expand Shrink
Total Count
環保署 19 19 0 0 1 0 0
39
經濟部 12 11 1 0 0 1 0
25
內政部 17 17 0 0 0 1 0
35
新聞局 13 11 2 0 1 1 0