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Comparison of Graph Distance Measures for Movie Similarity Using a Multilayer Network Model.

Majda Lafhel1, Hocine Cherifi2, Benjamin Renoust3

  • 1FLSH, LRIT, FS, Mohammed V University in Rabat, Rabat 10090, Morocco.

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Summary
This summary is machine-generated.

This study introduces a multilayer network model to analyze movie stories using character, keyword, and location data. It reveals how graph distance measures can identify genre similarities and classify movies, enhancing recommendation systems.

Keywords:
graph distance measuremoviemovie genre classificationmultilayer networknetwork quantificationnetwork similarity

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Area of Science:

  • Graph theory
  • Network analysis
  • Computational linguistics

Background:

  • Graph distance measures are key for comparing graph structures.
  • Movie network analysis traditionally uses single-layer models.
  • A comprehensive multilayer network model is needed for richer story representation.

Purpose of the Study:

  • To develop a multilayer network model for movie stories.
  • To quantify movie story similarity using layer-to-layer distance measures.
  • To explore graph distance measures for genre-based movie analysis and classification.

Main Methods:

  • Constructing multilayer networks from movie scripts (character, keyword, location).
  • Applying five graph distance measures: network portrait divergence, NetLSD, NetMF, Laplacian spectra, and D-measure.
  • Analyzing movie similarities within and across genres (sci-fi, horror, romance, comedy).

Main Results:

  • Different graph distance measures effectively identify similarities among movies within the same genre.
  • The methods show varying success in classifying movies based on genre.
  • The effectiveness of a method depends on its ability to capture the network's inherent structure.

Conclusions:

  • Multilayer network models provide a more comprehensive approach to movie story analysis.
  • Graph distance measures are valuable tools for understanding genre-based movie similarities.
  • The proposed methodology can be integrated into movie recommendation systems for improved performance.