期刊
SOCIAL NETWORK ANALYSIS AND MINING
卷 9, 期 1, 页码 -出版社
SPRINGER WIEN
DOI: 10.1007/s13278-019-0577-7
关键词
Social network analysis; New Testament; Power law with exponential cutoff; Degree distribution; Network alignment; Bootstrapping; Network tree
The four canonical Gospels represent parallel narratives from multiple sources, and their relationship to each other has been studied using a variety of textual approaches. Here, social network analysis techniques are applied to social networks derived from the New Testament narratives contained in the Gospels and Acts, in order to explore their structure and relationships. Using maximum likelihood estimation, the degree distributions of the social networks of a Combined Gospel comprised of the four individual Gospels, and Acts, both approximate to a power law with exponential cutoff. The heavy-tailed forms of the degree distributions are emergent from the process of composition, and amalgamation of sources, and represent a complex process of self-organization. Nodes (characters) that deviate from the best fit line are shown to identify unusual or key features in a narrative. The use of network alignments, utilizing edge identity, is introduced in order to measure similarity between social networks. This approach allows the construction of a pairwise distance matrix for the four individual Gospels social networks, from which a phylogenetic tree' of social network relatedness can be generated. Using minimum evolution, least squares and neighbor-joining tree construction approaches, a Gospel phylogenetic tree' was calculated, utilizing nonparametric bootstrapping as a test for tree stability. The analysis recovered the close relationship between the three synoptic Gospels, with Mark most closely related to Matthew, consistent with the proposed heavy borrowing from Mark during the composition of Matthew. The Gospel of John is recovered as an outgroup, reflecting its divergent nature. Consequently, the network tree methodology appears a promising technique for delineating similarity between empirical networks in general, and a range of potential applications are described.
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