By Lu Huang
Introduction
This blog post is the result of Temple University’s Graduate Certificate in Cultural Analytics Practicum course, for which I developed a preliminary research project on applying the method of social network analysis to the transmission of Abhidharma Buddhism in early medieval China. I am a PhD candidate at the department of Religion in Temple University with a focus on Buddhist studies. Before coming to Temple I spent several years in various Buddhist monasteries learning Buddhist philosophy. I first learned about the method of Historical Social Network Analysis from Dr. Marcus Bingenheimer. During the Practicum course this fall, with the help of Dr. Alex Wermer-Colan, I learned more about how to use Gephi to analyze the Abhidarma transmission social network.
Network Data on Buddhist Monks
The dataset used in this paper is the “Historical Social Network of Chinese Buddhism,” and can be downloaded from Github. This dataset extracts data from nine texts, including Collection of Records from the Tripitaka (Chu sanzang jiji 出三藏記集), Excerpts from ‘Biographies of Famous Monks’ (Mingseng zhuan chao名僧傳抄), Biographies of Nuns (Biqiuni zhuan比丘尼傳) and another six Biographies of eminent monks. With more than 18000 nodes and their connections, this network dataset covers more than 2000 years of Chinese Buddhist history.
Apart from names of the person, it also contains data such as their birth/death year, the dynasty they belong to, and most importantly, the other individuals with whom they are connected. It does not contain more detailed information such as what each monk studied or where they conducted these studies. The edge does have a weight, but according to Bingenheimer (2021, p. 236), the weights in the dataset have little analytical value.
Enhancing the Network Dataset
What I added to the dataset is the information about monks’ activities related to Abhidharma. I have collected information from Biographies of eminent monks, and also individual texts such as Beishan lu 北山錄. I added several columns to the dataset indicating whether a monk has studied a specific Abhidharma text, such as Apitan xin lun 阿毗曇心論, or not. I also added another column to indicate whether a monk has studied Abhidharma or not.
Data Visualization
The software I use to explore this network data is Gephi. This is an open source software for social network analysis.
I first downloaded the .gephi file of “Historical Social Network of Chinese Buddhism.” Then I exported the csv file and added the information about Abhidharma into this data. Afterwards I import the data back to the .gephi file.
Using the color panel for the nodes, I colored the data according to whether the monk has engaged in some kind of Abhidharma or not. Since most of the monks who study Abhidharma centered in the first millennium of the transmission of Buddhism, I used the “square” button in the “overview” to select those monks in this period. The following is a display of monks in the first few centuries with those who have studied Abhidharma colored to red.
Betweenness Centrality
From this graph we can see that although only a small portion of the monks study Abhidharma, their relevance is rather significant. The nodes are sized according to their betweenness centrality.
As Mark NJ Newman states, “Betweenness centrality can be regarded as a measure of the extent to which an actor has control over information flowing between others. In a network in which flow is entirely or at least mostly along geodesic paths, the betweenness of a vertex measures how much flow will pass through that particular verte.” (Newman 2005, P.2). This means that relatively speaking, monks who have studied Abhidharma have more control over information flowing between other monks.
Future research is needed regarding the reason why Abhidharma monks have relatively higher betweenness centrality. A possible reason is that these monks usually study both Mahāyāna and Abhidharma texts, and have relatively more information flowing through them due to connections with monks with different academic interests.
The following table shows the monks that come from the Northern and Southern dynasties who have the highest betweenness centrality. It can be seen that most monks who have high centrality have studied Abhidharma.
The above image is the first few lines of the table that has nodes with the highest betweenness centrality. The nodes highlighted as red are lay people. The nodes highlighted as yellow have once studied Abhidharma. It can be seen that apart from influential patrons such as Xiao Yan and the Wu Emperor of Liang, most of the other top nodes have studied Abhidharma.
Ego Network
Another function I have tried in Gephi is the ego network measurements. This method focuses on a network from the perspective of a single user/ego with all its 1st-degree connections. In other words, ‘Ego Networks’ focus on one node and its direct connections, revealing its immediate network.
The following is part of the ego network of Huiguan. I choose Huiguan since he receives relatively less attention in traditional historiography of early medieval Chinese Buddhism by modern scholars.
We can see that he is the next largest node after Huiyuan, who has a relatively high betweenness centrality and also studies Abhidharma. He is connected to several influential nodes, such as Kumarajiva and Huiyuan. He is also important in cross-generation knowledge transmission since he connects with both important nodes in the last generation and nodes in the next generation.
Conclusion
This is a preliminary research project on applying the method of social network analysis to the transmission of Abhidharma Buddhism in early medieval China. Based on Dr. Bingenheimer’s “Historical Social Network of Chinese Buddhism,” I have added information about whether a monk has studied Abhidharma or not, as well as the specific text he studies.
One prominent phenomena revealed by the network is that nodes who study Abhidharma have relatively high betweenness centrality, which means that there are relatively more information flowing through them. I have also tried the function of ego network, such as the 1st degree network of Huiguan, a monk with relatively high betweenness centrality, who has not been given much attention in traditional historiography of early medieval Chinese Buddhism.
As next steps in this research project, I will try to analyze his connections and see the role he plays in both Abhidharma transmission and the transmission of other Buddhist thought.
References
Primary Sources
Chu sanzang jiji 出三藏記集. 510 C.E. by Sengyou僧祐 (445-518). Vol. 55, no. 2145, in Taishō shinshū daizōkyō 大正新修大藏經. Edited by Takakusu Junjirō 高楠順次郎 and Watanabe Kaigyoku 渡邊海旭 et al. (1924–1932). 85 vols. Tōkyō: Taishō issaikyō kankōkai 大正一切經刊刻會 (CBETA version).
Mingseng zhuan chao名僧傳抄. 510 C.E. by Baochang寶唱 (502-557). Vol. 77, no. 1523, in Shinsan Dainihon zokuzōkyō 卍新纂大日本續藏經. Edited by Kawamura Kōshō 河村考照. (1975–1989). Printed by Kokusho kangyōkai 國書刊行會. Originally compiled by Nakano Tatsue 中野達慧 (1905–1912). Kyōtō: Zōkyō shoin 藏經書院 (CBETA version).
Biqiuni zhuan比丘尼傳. 516 C.E. By Baochang寶唱 (502-557). Vol. 50, no. 2063, in Taishō shinshū daizōkyō 大正新修大藏經.
Beishan lu 北山錄. 806 C.E. Authored by Shenqing神清. Annotated by Huibao慧寶. Vol. 52, no. 2113, in Taishō shinshū daizōkyō 大正新修大藏經.
Secondary Sources
Bingenheimer, Marcus. 2021. “The Historical Social Network of Chinese Buddhism.” Journal of Historical network research 5 (1). https://doi.org/10.25517/jhnr.v5i1.119.
Newman, Mark EJ. “A measure of betweenness centrality based on random walks.” Social networks 27, no. 1 (2005): 2.