數位典藏與數位人文國際研討會（第9屆）=International Conference of Digital Archives and Digital Humanities (9th)
出版日期
2018.12.18
頁次
42 - 42
出版者
臺灣數位人文學會
出版地
臺北市, 臺灣 [Taipei shih, Taiwan]
資料類型
期刊論文=Journal Article
使用語言
英文=English
附註項
1. Bingenheimer, Marcus: Temple University. 2. The evolving dataset is available at: http://mbingenheimer.net/tools/socnet/index.html
關鍵詞
Social Networkds Analysis; Chinese Buddhism; Buddhist history in China
摘要
This presentation demonstrates first results of an ongoing project that aims to establish a dataset for the historical social network analysis for the study of Chinese Buddhist history. There are two main sources for the data: Buddhist biographical literature on eminent monks (gaoseng zhuan 高僧傳) and lineage data connecting masters and students. The former is especially rich for the time between 300 and 1000, when the major gaoseng zhuan collections allow us to situate people in place and time and trace their relationships. The lineage data is extracted mainly from the literature of the Chan school (collected sayings (yulu 語錄), lamp- transmission (denglu 燈錄)), temple gazetteers (shanzhi 山志, sizhi 寺志), and other forms of Buddhist historiography. The data is made openly available and is distributed and archived online, while annual workshops are conducted to train graduate students in using data and tools. Our datasets are so far the most comprehensive tool to view Chinese Buddhist history in network terms. Zooming into regions of the network historians can research the relationships between players at a time, as each relationship-link between actors is referenced to canonical and para- canonical sources. We are now exploring how traditional network measures such as different forms of centrality, or techniques such as clique identification can be put to use to improve our understanding of Buddhist history in East Asia. Research questions include: How can different centrality measures help to guide a historian’s search for who is “important” in Chinese history? How bad is the gender gap in Buddhist history and how can it be visualized in a network? What connections are there between the structure of the sources and the topology of the network? Does the algorithmic identification of communities allow us to discover hitherto unknown cliques?
