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IJMLC 2013 Vol.3(1): 107-111 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.282

Complex System Analysis of Social Networks Extracted from Literary Fictions

Gyeong-Mi Park, Sung-Hwan Kim, Hye-Ryeon Hwang, and Hwan-Gue Cho

Abstract—Recently we witnessed that the social network analysis focusing on social entities is applied in the social science and web-science, behavioral sciences, as well as in economics, marketing. In this paper we present one method to construct the social network from literary fictions by a simple lexical analysis, not using the complex natural language processing tools. And we will show that those social graphs, saying literary social graph, shows the power law distribution of some features, which is the typical characteristics of complex systems. We showed that the social network extracted from literary data reflects the similar network structure which was semantically designed by authors of fictions. And we newly proposed the concept of the kernel of literary social network by which we can classify the abstract level of protagonists appeared in fictions. Our study shows that the metric distance among characters written in linear text is very similar to the intrinsic and semantic relationship described by fiction writers, which implies the proposed social network from fictions could be another representation of literary fiction. So we can apply other scientific and quantitative approach by analyzing the concrete social graph model extracted from textual data.

Index Terms—Social network, complex system, literary fiction, character graph, power law.

Gyeong-Mi Park, Sung-Hwan Kim and Hwan-Gue Cho are with Dept. of Computer Engineering, Pusan National University, Busan, South Korea (e-mail: miya11@pusan.ac.kr; sunghwan@pusan.ac.kr; hgcho@pusan.ac.kr).


Cite:Gyeong-Mi Park, Sung-Hwan Kim, Hye-Ryeon Hwang, and Hwan-Gue Cho, "Complex System Analysis of Social Networks Extracted from Literary Fictions," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 107-111, 2013.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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