在耐延遲網路環境下節點的相遇情況不是很頻繁，這可能導致節點間的連線斷斷續續，使得有效地將訊息傳遞成為一件困難的事情。藉由社群感知轉送機制的中間度指標特性，可以來提升傳送成功率。雖然大多數研究幾乎都是將訊息轉送到單一目的地或是多個且已知的目的地。然而，一些應用像是廣告的散佈，要將訊息送給對訊息有興趣的人，但卻不知道是誰。因此，關鍵的問題為如何建立社群網路關係的親密度機制，來選擇作為轉送訊息的節點，並利用群體廣播的方式盡可能有效地傳播至最多可能目標目的地，進而提升效能。 本論文以群播機制和社群感知當作基礎概念，來設計新的轉送訊息的方法和公式化選擇中繼節點的機制。最後，我們使用政治大學實際軌跡來模擬，將模擬結果與其它路由演算法比較，其結果證明我們所提出的方法能提高訊息傳送成功率和正確率，降低傳送延遲時間和傳送訊息的成本。 / In delay tolerant networks (DTNs), nodes infrequently encounter with each others. This results in intermittent connectivity of the nodes, and makes it difficult to deliver the message effectively. A social-aware forwarding scheme can help for successful delivery ratio by utilizing the characteristic of their centrality metric. Most of the previous studies focus on message delivery to single destination or some priori known destinations. However, some applications like advertisement dissemination may not know who will be the interested persons to be delivered. Therefore, the key challenge is how to establish the social relationship strategy to select appropriate nodes as relays, and furthermore to use multicasting to disseminate effectively as many “target” destinations as possible to improve the performance. This thesis developed a new strategy which has a new forwarding message scheme and formulates the selection of the relay nodes based on the concept of the multicasting and the social network. Finally, we used the reality trace data of National Chengchi University to simulate. The simulation results are compared to others DTNs routing protocols as well as other social-aware forwarding schemes. The results showed that our proposed approach can enhance the successful delivery ratio and delivery accuracy, decrease the delivery delay and reduce the delivery overhead.

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.

近幾年在金融風暴及全球競爭等影響下，企業紛紛導入商業智慧平台，提供管理階層可簡易且快速的分析各種可量化管理的關鍵指標。但在後續的推廣上，經常會因商業智慧系統提供的資訊過於豐富，造成使用者在學習階段無法有效的取得所需資訊，導致商業智慧無法發揮預期效果。本論文以使用者在商業智慧平台上的操作相似度進行分析，建立相對於實體部門的凝聚子群，且用中心性計算各節點的關聯加權，整合至所設計的推薦機制，用以提升商業智慧平台成功導入的機率。經模擬實驗的證實，在推薦機制中考慮此因素會較原始的推薦機制擁有更高的精確度。 / In recent years, enterprises are facing financial turmoil, global competition, and shortened business cycle. Under these influences, enterprises usually implement the Business Intelligence platform to help managers get the key indicators of business management quickly and easily. In the promotion stage of such Business Intelligence platforms, users usually give up using the system due to huge amount of information provided by the BI platform. They cannot intuitively obtain the required information in the early stage when they use the system. In this study, we analyze the similarity of users’ operations on the BI platform and try to establish cohesive subgroups in the corresponding organization. In addition, we also integrate the associated weighting factor calculated from the centrality measures into the recommendation mechanism to increase the probability of successful uses of BI platform. From our simulation experiments, we find that the recommendation accuracies are higher when we add the clustering result and the associated weighting factor into the recommendation mechanism.