Indu Rodda
ABSTRACT
The main goal of this work is to visualize a novel using ideas from social network analysis. A novel can be represented as a character network by using the novel’s characters as nodes and the interactions between them as edges. Communities of each chapter can be used to visualize how the characters come together and move away across the novel. One of the main challenges is to match the communities between two consecutive timestamps. This helps in detecting new communities as well as the dynamics of the communities as the story progresses in the novel. We define a similarity score that captures the dynamics of the community transitions and helps us in designing a matching algorithm. Further, a novel coloring scheme is proposed so that the viewer can see the merging or splitting of the communities smoothly. The algorithm is validated using some important events in the novel by observing the transitioning of the communities and nodes shifting across communities.
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Cross Ref
- Rémy Cazabet and Frédéric Amblard. 2014. Dynamic Community Detection. Springer New York, New York, NY, 404–414.Google Scholar
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- Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, and Rong Jin. 2011. Detecting communities and their evolutions in dynamic social networks—a Bayesian approach. Machine learning 82, 2 (2011), 157–189.Google Scholar
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