當面對總體蘊藏資訊量極為龐大，單筆資訊則較為零碎的資料來源時，社會網路分析同時兼具微觀及巨觀分析特點的方式，提供了一獨特的切入分析角度。目前在社會網路分析領域中，針對網路性質所發展的分析指標等相關理論，大多以1-mode網路為主要的探討模式，對於2-mode網路模式下的著墨則較為稀少。在本研究論文中，以政府人事異動資料庫為主要資料來源，建構出同時具有人物以及職務兩種不同類型節點的2-mode網路，並選擇以適用於2-mode網路模式下的連結預測理論為主要核心，建置出職務接替人選預測系統，其後透過配合不同的實驗模式設計進行接替人選的預測行動，希望能以此來探討單一職位對於其未來接替人選的考量上，受到其歷任人員職務歷程的影響程度。 實驗數據結果顯示，本研究所建置出的接替人選預測系統，對於不同的職務分類層級以及針對不同部門間的職務預測，均會產生不同的預測成效，而這些成果均可適切反映出因應於升遷法制規範或是部份部門於專業知識上的需求條件，因而使得其在對於職位接替人選的考量上，較易受到歷任人選於職務歷程方面的影響。 / When coping with the information source which can store quite high information load in total while one piece of that tends to be fragmentary, Social Network Analysis provides a unique viewpoint to contain analyzing characteristics from both of microcosmic and macrocosmic perspectives. To date, when it comes to theories related to analysis indicator established from different analyzing characteristics in the field of Social Network Analysis, effort is mainly made to explore 1-mode network. By contrast, little emphasis is put on 2-mode network. In this study, the database of government’s personnel change is adopted as the major information source. This study then establishes a 2-mode network with two different types of node, that is, personnel and position. Choosing Link Prediction Theory as the major core with its applicability of 2-mode network, the present study sets up a prediction system of position taking-over candidate. Then, in accordance with different designs of experimental model, the prediction is conducted in an attempt to investigate whether the consideration of future taking-over candidate for a certain position would be influenced by position courses of all past personnel in this position. According to the experimental data, the prediction system of position taking-over candidate established in this study shows different prediction efficiency when predicting different position layers and positions from different sectors. These results all appropriately reflect that the consideration of position taking-over candidate is more easily influenced by position courses of past personnel due to the rules of promotion and the conditions of professional knowledge in some sectors.

隨著資訊科技的發達，資訊成長的速度日以倍計，對於大量且片斷的資料，社會網路分析(Social Network Analysis)提供我們可能的研究方法。社會網路主要是由節點以及節點彼此間的連結所形成的網路結構，透過社會網路分析和連結預測理論，我們可以從微觀與巨觀的切入角度，來進行龐大資料量的政府人事異動資料庫進行研究分析。本論文研究，將政府人事異動資料庫中的異動記錄建構為人物與職務兩類不同的社會網路結構，並透過社會網路分析以及連結預測，來發掘人物與不同職務之間的相互影響性，並進一步分析在特定職務的實際接任人選上，實際被影響的因素為何。實驗結果呈現本研究所設計出的模型，對於政府人事異動的互動關係在不同角度的觀察上有所幫助，也從中可以發現在實際接任人選上的考量上，歷任人選的歷任職務有相當程度的影響性，並瞭解社會網路分析與連結預測在實際情境應用下的可能性與限制性。 / Information grows up in very fast way with the advancement in information technology. SNA (Social Network Analysis) provides the possible research ways for the large number of fragmentary information. Social network is the network structure which constructed by the links of each nodes in it. Through SNA (Social Network Analysis) and Link Prediction theory, we can investigate government official's succession database with huge amount of data from micro and macro perspectives. The objective of this study is the construction of two different types of person and position social network structures and the exploration of the interaction between the person and position nodes through link prediction theory. We also discover the impact factors for actual appointee of specific position in further analysis. The study result shows the design model helps us to observe the interaction in government official's succession from different perspectives. We found that is great influence of successive positions of successive candidates in consideration of actual appointee.

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.