對於零碎且結構複雜的資料來源時，社會網路分析能夠給予整體性的觀察，還能檢視個體之間的關係。目前社會網路分析研究中，因為將網路退化至簡單連結關係，所以會遺失許多珍貴的資訊。而網路規模和型態隨著研究議題的不同，也會跟著增大或趨於多變，但動態網路分析能夠提供我們檢視每個時期，網路的變化或社群的形成或消失，甚至能知道節點間的互動影響。本論文研究，以政府人事異動資料為主，並且加入了其他政府組織的相關資料，建構出政府組織的從屬網路，並在每個網路快照中，擷取出重要的官員異動；每一筆人事異動都是一個事件的發生，而特任或簡任官員在本研究中視為重要事件，從這些重要事件的發生，我們能夠對每個時間的官員，使用EventRank的演算法做排名計算。最後能從時間的變化中，觀察出每個時期的佔有重要影響力的官員。 / To fragmented and complex structure data, social network analysis (SNA) can give an overall observation, but also view the relationship between individuals. Recent research in SNA is the degradation of the network link to a simple relationship but it will lose a lot of valuable information. The size and type of network with different research topics will follow the increase or rapidly changing, dynamic network analysis can provide our view of changes in the network or community to form or disappear in every period, even know the impact of the interaction between nodes. This thesis is based on the government official changes and other related data to construct manager-subordinate network of the government organization and capture the important interactions between officials in every network snapshot. An official change is the occurrence of an event and special level official changes in this study as a critical event. From these critical events, we can use the Event Rank algorithm to rank the officials. Finally, we can observe which official has more influence from the time changes.