Unsupervised cluster analyses of character networks in fiction: Community structure and centrality

Abstract

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.