Skip to main content

Movie Script Similarity Using Multilayer Network Portrait Divergence

  • Conference paper
  • First Online:
Complex Networks & Their Applications IX (COMPLEX NETWORKS 2020 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 943))

Included in the following conference series:

Abstract

This paper addresses the question of movie similarity through multilayer graph similarity measures. Recent work has shown how to construct multilayer networks using movie scripts, and how they capture different aspects of the stories. Based on this modeling, we propose to rely on the multilayer structure and compute different similarities, so we may compare movies, not from their visual content, summary, or actors, but actually from their own storyboard. We propose to do so using “portrait divergence”, which has been recently introduced to compute graph distances from summarizing graph characteristics. We illustrate our approach on the series of six Star Wars movies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Here is a quick summary of the plot: The saga follows Anakin Skywalker, a young child freed from slavery to become a Jedi and endeavored to save the galaxy. Anakin instructed by the Jedi Masters of the light side, married the senator Padme. Unfortunately, the Sith (Palpatine) submits him to the dark side, rebelling and losing against his Master (Obi-Wan). Anakin is saved by the Sith, now ruling over the galaxy, and transformed to Darth Vader. Padme died while giving birth to twins Luke and Leia. Luke becomes a farmer while Leia becomes a princess. Nineteen years later, Obi-Wan met Luke and taught him the Jedi way, while receiving a distress call from the princess Leia, leading the resistance against Palpatine. Joining smuggler Han Solo in the Millenium Falcon they went to save her, and support the resistance. Luke completes his Jedi training, while Solo gets captured by the Sith, who crushes most of the resistance. Vader tries to turn Luke to the dark side when discovering that Luke is his son. Unsuccessful, Palpatine tries to kill Luke, awaking in Vader his old self. Vader turns back against Palpatine and rescues the galaxy.

References

  1. Bagavathi, A., Krishnan, S.: Multi-net: scalable multilayer network embeddings. arXiv preprint arXiv:1805.10172 (2018)

  2. Bagrow, J.P., Bollt, E.M.: An information-theoretic, all-scales approach to comparing networks. Appl. Netw. Sci. 4(1), 45 (2019)

    Article  Google Scholar 

  3. Bagrow, J.P., Bollt, E.M., Skufca, J.D., Ben-Avraham, D.: Portraits of complex networks. EPL (Europhys. Lett.) 81(6), 68004 (2008)

    Article  Google Scholar 

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  5. Bougiatiotis, K., Giannakopoulos, T.: Content representation and similarity of movies based on topic extraction from subtitles. In: Proceedings of the 9th Hellenic Conference on Artificial Intelligence, pp. 1–7 (2016)

    Google Scholar 

  6. Cozzo, E., Kivelä, M., De Domenico, M., Solé-Ribalta, A., Arenas, A., Gómez, S., Porter, M.A., Moreno, Y.: Structure of triadic relations in multiplex networks. New J. Phys. 17(7), 073029 (2015)

    Article  Google Scholar 

  7. Deldjoo, Y., Schedl, M., Elahi, M.: Movie genome recommender: a novel recommender system based on multimedia content. In: 2019 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1–4. IEEE (2019)

    Google Scholar 

  8. Demirkesen, C., Cherifi, H.: A comparison of multiclass SVM methods for real world natural scenes. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 752–763. Springer, Heidelberg (2008)

    Google Scholar 

  9. Grabska-Gradzińska, I., Kulig, A., Kwapień, J., Drożdż, S.: Complex network analysis of literary and scientific texts. Int. J. Mod. Phys. C 23(07), 1250051 (2012)

    Article  Google Scholar 

  10. Kovacs, B., Kleinbaum, A.M.: Language-style similarity and social networks. Psychol. Sci. 31(2), 202–213 (2020)

    Article  Google Scholar 

  11. Labatut, V., Bost, X.: Extraction and analysis of fictional character networks: a survey. ACM Comput. Surv. (CSUR) 52(5), 1–40 (2019)

    Article  Google Scholar 

  12. Lasfar, A., Mouline, S., Aboutajdine, D., Cherifi, H.: Content-based retrieval in fractal coded image databases. In: Proceedings 15th International Conference on Pattern Recognition, ICPR-2000, vol. 1, pp. 1031–1034. IEEE (2000)

    Google Scholar 

  13. Lee, O.J., Jo, N., Jung, J.J.: Measuring character-based story similarity by analyzing movie scripts. In: Text2Story@ ECIR, pp. 41–45 (2018)

    Google Scholar 

  14. Lee, O.J., Jung, J.J.: Explainable movie recommendation systems by using story-based similarity. In: IUI Workshops (2018)

    Google Scholar 

  15. Markovič, R., Gosak, M., Perc, M., Marhl, M., Grubelnik, V.: Applying network theory to fables: complexity in Slovene Belles-Lettres for different age groups. J. Complex Netw. 7(1), 114–127 (2018)

    Article  Google Scholar 

  16. Mourchid, Y., Renoust, B., Cherifi, H., El Hassouni, M.: Multilayer network model of movie script. In: International Conference on Complex Networks and their Applications, pp. 782–796. Springer (2018)

    Google Scholar 

  17. Mourchid, Y., Renoust, B., Roupin, O., Văn, L., Cherifi, H., El Hassouni, M.: Movienet: a movie multilayer network model using visual and textual semantic cues. Appl. Netw. Sci. 4(1), 1–37 (2019)

    Article  Google Scholar 

  18. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)

    Article  Google Scholar 

  19. Park, S.B., Oh, K.J., Jo, G.S.: Social network analysis in a movie using character-net. Multimed. Tools Appl. 59(2), 601–627 (2012)

    Article  Google Scholar 

  20. Pastrana-Vidal, R., Gicquel, J., Blin, J., Cherifi, H.: Predicting subjective video quality from separated spatial and temporal assessment. In: Human Vision and Electronic Imaging XI, vol. 6057, p. 60570S. SPIE (2006)

    Google Scholar 

  21. Reddy, S., Nalluri, S., Kunisetti, S., Ashok, S., Venkatesh, B.: Content-based movie recommendation system using genre correlation. In: Smart Intelligent Computing and Applications, pp. 391–397. Springer (2019)

    Google Scholar 

  22. Renoust, B., Kobayashi, T., Ngo, T.D., Le, D.D., Satoh, S.: When face-tracking meets social networks: a story of politics in news videos. Appl. Netw. Sci. 1(1), 4 (2016)

    Article  Google Scholar 

  23. Renoust, B., Le, D.D., Satoh, S.: Visual analytics of political networks from face-tracking of news video. IEEE Trans. Multimed. 18(11), 2184–2195 (2016)

    Article  Google Scholar 

  24. Škrlj, B., Renoust, B.: Patterns of multiplex layer entanglement across real and synthetic networks. In: International Conference on Complex Networks and Their Applications, pp. 671–683. Springer (2019)

    Google Scholar 

  25. Tan, M.S., Ujum, E.A., Ratnavelu, K.: A character network study of two sci-fi tv series. In: AIP Conference Proceedings, vol. 1588, pp. 246–251. AIP (2014)

    Google Scholar 

  26. Tantardini, M., Ieva, F., Tajoli, L., Piccardi, C.: Comparing methods for comparing networks. Sci. Rep. 9(1), 17557 (2019). https://doi.org/10.1038/s41598-019-53708-y

    Article  Google Scholar 

  27. Waumans, M.C., Nicodème, T., Bersini, H.: Topology analysis of social networks extracted from literature. PLoS ONE 10(6), e0126470 (2015)

    Article  Google Scholar 

  28. Zhou, H., Hermans, T., Karandikar, A.V., Rehg, J.M.: Movie genre classification via scene categorization. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 747–750 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Majda Lafhel or Hocine Cherifi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lafhel, M., Cherifi, H., Renoust, B., El Hassouni, M., Mourchid, Y. (2021). Movie Script Similarity Using Multilayer Network Portrait Divergence. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-65347-7_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65346-0

  • Online ISBN: 978-3-030-65347-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics