Multilayer Network Model of Movie Script

  • Youssef MourchidEmail author
  • Benjamin Renoust
  • Hocine Cherifi
  • Mohammed El Hassouni
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)


Network models have been increasingly used in the past years to support summarization and analysis of narratives, such as famous TV series, books and news. Inspired by social network analysis, most of these models focus on the characters at play. The network model well captures all characters interactions, giving a broad picture of the narration’s content. A few works went beyond by introducing additional semantic elements, always captured in a single layer network. In contrast, we introduce in this work a multilayer network model to capture more elements of the narration of a movie from its script: people, locations, and other semantic elements. This model enables new measures and insights on movies. We demonstrate this model on two very popular movies.


Script Multilayer networks Narration Movie 


  1. 1.
    Tan, M.S.A., Ujum, E.A., Ratnavelu, K.: A character network study of two sci-fi TV series. In: AIP Conference Proceedings, vol. 1588, No. 1. AIP (2014)Google Scholar
  2. 2.
    Kivela, M., et al.: Multilayer networks. J. Complex Netw. 2(3), 203–271 (2014)Google Scholar
  3. 3.
    Mish, B.: Game of Nodes: A Social Network Analysis of Game of Thrones (2016)Google Scholar
  4. 4.
    Kadushin, C.: Understanding social networks: theories, concepts, and findings. OUP, USA (2012)Google Scholar
  5. 5.
    Renoust, B., et al.: A social network analysis of face tracking in news video. In: 2015 11th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS). IEEE (2015)Google Scholar
  6. 6.
    Renoust, B., Melanon, G., Viaud, M.-L.: Entanglement in multiplex networks: understanding group cohesion in homophily networks. In: Cham, Social Network Analysis-Community Detection and Evolution, pp. 89–117. Springer (2014)Google Scholar
  7. 7.
    Waumans, M.C., Nicodme, T., Bersini, H.: Topology analysis of social networks extracted from literature. PloS one 10(6), e0126470 (2015)Google Scholar
  8. 8.
    Jhala, A.: Exploiting: structure and conventions of movie scripts for information retrieval and text mining. Springer, Berlin, Heidelberg (2008)Google Scholar
  9. 9.
    Yeung, M., Yeo, B.-L., Liu, B.: Extracting story units from long programs for video browsing and navigation. In: Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems, 1996. IEEE (1996)Google Scholar
  10. 10.
    Jung, B., et al.: Narrative abstraction model for story-oriented video. In: Proceedings of the 12th Annual ACM International Conference on Multimedia. ACM (2004)Google Scholar
  11. 11.
    Weng, C.-Y., Chu, W.-T., Ja-Ling, W.: Rolenet: movie analysis from the perspective of social networks. IEEE Trans. Multimed. 11(2), 256–271 (2009)Google Scholar
  12. 12.
    Park, S.-B., Kyeong-Jin, O., Jo, G.-S.: Social network analysis in a movie using character-net. Multimed. Tools Appl. 59(2), 601–627 (2012)Google Scholar
  13. 13.
    Kipling, R.: Just So Stories, 1902. Initial letter by (2004)Google Scholar
  14. 14.
    Flint, L.N.: Newspaper writing in high schools: containing an outline for the use of teachers. Pub. from the Department of journalism Press in the University of Kansas (1917)Google Scholar
  15. 15.
    Omran, A., Fouad, N.A., Christoph, T.: Choosing an NLP library for analyzing software documentation: a systematic literature review and a series of experiments. In: Software Repositories. IEEE Press (2017)Google Scholar
  16. 16.
    Blei, D.M., Andrew, Y.N., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)Google Scholar
  17. 17.
    Li, J., Zhang, K.: Keyword extraction based on tf/idf for Chinese news document. Wuhan Univ. J. Nat. Sci. 12(5), 917–921 (2007)Google Scholar
  18. 18.
    Yuepeng, L., Cui, J., Junchuan, J.: A keyword extraction algorithm based on Word2vec. E-sci. Technol. Appl. 4, 54–9 (2015)Google Scholar
  19. 19.
    Bioglio, L., Pensa, R.G.: Is this movie a milestone? identification of the most influential movies in the history of cinema. In: International Workshop on Complex Networks and their Applications, Springer, Cham (2017)Google Scholar
  20. 20.
    Rital, S., Cherifi, H., Miguet, S.: Weighted adaptive neighborhood hypergraph partitioning for image segmentation. Springer, Berlin, Heidelberg (2005)Google Scholar
  21. 21.
    Demirkesen, C., Cherifi, H.: A comparison of multiclass SVM methods for real world natural scenes. In: Concepts for Intelligent Vision Systems. Springer, Berlin, Heidelberg (2008)Google Scholar
  22. 22.
    Chen, B.-W., Wang, J.-C., Wang, J.-F.: A novel video summarization based on mining the story-structure and semantic relations among concept entities. IEEE Trans. Multimed. 11(2), 295–312 (2009)Google Scholar
  23. 23.
    Pastrana-Vidal, R.R., et al.: Predicting subjective video quality from separated spatial and temporal assessment. In: Human Vision and Electronic Imaging XI., vol. 6057. International Society for Optics and Photonics (2006)Google Scholar
  24. 24.
    Domenico, M.D., Porter, M.A., Arenas, A.: Multilayer analysis and visualization of networks. J. Complex Netw. 10 (2014)Google Scholar
  25. 25.
    Lucas, G.: Star Wars, Episode IV: A New Hope. Twentieth Century Fox Home Entertainment (2006)Google Scholar
  26. 26.
    Whedon, J., Downey, R., Jr.: The Avengers. Walt Disney Studios Home Entertainment (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Youssef Mourchid
    • 1
    Email author
  • Benjamin Renoust
    • 2
  • Hocine Cherifi
    • 3
  • Mohammed El Hassouni
    • 1
    • 4
  1. 1.Faculty of Sciences, LRIT-CNRST URAC 29Rabat IT Center, Mohammed V UniversityRabatMorocco
  2. 2.Institute for Datability Science, Osaka UniversityOsakaJapan
  3. 3.LE2I UMR 6306 CNRSUniversity of BurgundyDijonFrance
  4. 4.DESTEC, FLSHMohammed V University in RabatRabatMorocco

Personalised recommendations