TY - JOUR
T1 - Unsupervised cluster analyses of character networks in fiction
T2 - Community structure and centrality
AU - Chen, R. H.G.
AU - Chen, C. C.
AU - Chen, C. M.
N1 - Funding Information:
RHGC carried out the study of the character network in Dream of the Red Chamber and drafted the manuscript. CCC compared existing versions of Dream of the Red Chamber and participated in the preparation of data and figures and the interpretation of results. CMC conceived of the study and wrote the main manuscript text. This work was supported in part by the Ministry of Science and Technology of Taiwan under grant no. MOST105-2112-M-003-003-MY3. CMC thanks R. Ng for the hospitality at the Department of Computer Science, University of British Columbia.
Funding Information:
RHGC carried out the study of the character network in Dream of the Red Chamber and drafted the manuscript. CCC compared existing versions of Dream of the Red Chamber and participated in the preparation of data and figures and the interpretation of results. CMC conceived of the study and wrote the main manuscript text. This work was supported in part by the Ministry of Science and Technology of Taiwan under grant no. MOST105-2112-M-003-003-MY3 . CMC thanks R. Ng for the hospitality at the Department of Computer Science, University of British Columbia.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - We present an integrated approach to cluster and visualize character networks in fiction with the aid of computational and statistical methods. An unsupervised clustering algorithm, minimum span clustering (MSC), was applied to cluster fictional characters at various characteristic resolutions based on their activities in the novel. As a demonstration, we study the character network in Dream of the Red Chamber, the greatest novel in Chinese literature. The character network of the novel is found to exhibit properties of scale-free and small-world networks. Based on unsupervised cluster analyses, we construct and visualize the community structure of the network, and find a three-tiered structure of core, secondary, and peripheral characters. By treating the network as a weighted graph, we further analyze the centralities of characters to determine their importance in the network, and find that betweenness centrality, as a measure of characters’ control over the flow of the narrative, is differentiated from other centrality measures for Dream of the Red Chamber. We believe that these analytic methods provide beneficial tools for applications such as autonomous novel writing.
AB - We present an integrated approach to cluster and visualize character networks in fiction with the aid of computational and statistical methods. An unsupervised clustering algorithm, minimum span clustering (MSC), was applied to cluster fictional characters at various characteristic resolutions based on their activities in the novel. As a demonstration, we study the character network in Dream of the Red Chamber, the greatest novel in Chinese literature. The character network of the novel is found to exhibit properties of scale-free and small-world networks. Based on unsupervised cluster analyses, we construct and visualize the community structure of the network, and find a three-tiered structure of core, secondary, and peripheral characters. By treating the network as a weighted graph, we further analyze the centralities of characters to determine their importance in the network, and find that betweenness centrality, as a measure of characters’ control over the flow of the narrative, is differentiated from other centrality measures for Dream of the Red Chamber. We believe that these analytic methods provide beneficial tools for applications such as autonomous novel writing.
KW - Centrality measures
KW - Community structure detection
KW - Computer-aided visualization
KW - Social network
KW - Unsupervised clustering
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U2 - 10.1016/j.knosys.2018.10.005
DO - 10.1016/j.knosys.2018.10.005
M3 - Article
AN - SCOPUS:85054751997
SN - 0950-7051
VL - 163
SP - 800
EP - 810
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
ER -