本論文整理及分析北魏末年至隋朝滅亡(531-618 A.D.)之事件-人物網路。
本論文使用了二十四正史中的北史(李延壽著)以及隋書(魏徵、王劭等著)作為主體並以周書(令狐德棻等著)、魏書(魏收著)作為輔助，將書中本紀內紀載北魏末期韓陵之戰至隋末民變為止所出現之所有事件與人物統整，以建立事件-人物關係網路。
藉由所建造之「事件-人物」網路，進一步建立「人物-人物」網路以及「事件-事件」網路，並計算各網路中節點之度中心性(degree Centrality)以及區域中心性(Local Centrality)後進行排名。除了節點之排名，為了瞭解整體網路性質，額外計算了同配異配性（assortativity）、平均聚集係數(average clustering coefficient)和平均最短距離，以及大小相同之隨機網路之係數並比較之。藉由節點的參數排名進一步比較著名的皇帝、大臣以及事件在數據化後之重要性是否與一般認知相同。
本論文進一步比較了漢人政權為主導且基本為和平年代的秦末至新莽滅亡(209 B.C.-23A.D.) [1]與胡漢衝突劇烈、戰爭頻繁的北魏後期至隋朝滅亡(531A.D.- 618A.D.)的網路參數，發現在人物-人物網路部分，儘管此二時代之背景明顯不同，但他們對應的人物-人物網路在平均聚集係數隨度的變化圖卻極為相似，這代表了在不同的時代背景之下，歷史網路仍然具有普適的特性。

We use network theory to analyze Chinese history from the end of Northern Wei (531 A.D. ) to the collapse of Sui Dynasty (581A.D.－618A.D) which is an era of civil wars and ethnic conflict. We use two of the Twenty-Four Histories, also known as Orthodox Histories: History of the Northern Dynasties(北史) written by Li Yanshou (李延壽) and Book of Sui (隋書) written by a team of prominent scholars leaded by Wei Zheng(魏徵). We recorded all the events from the Annals part of two books to make a connection with participants in each event to build an Event-Person networks. From Event-Person networks, we also establish Event-Event network and Person-Person network. We calculate several parameters of networks, including degree centrality, local centrality, average clustering coefficient and assortativity to quantize the networks. Ranking the nodes (which is event or person in different network), we can understand the importance of persons or events from late Northern Wei to Sui Dynasty. Since the era we chose is an age of civil war and ethnic conflict, we would like to know the difference between age of more peace and age of frequent wars. We compare the date from the end of Qin Dynasty to the Collapse of Xing Mang (209 B.C. to 23 A.D.) which is Han-dominated regime with less wars. Surprisingly, the graphs of average clustering coefficient as a function of degree of both periods have very similar distributions which means that historical data can also show universal behavior.

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