6533b7d2fe1ef96bd125f563
RESEARCH PRODUCT
Movie Script Similarity Using Multilayer Network Portrait Divergence
Hocine CherifiMohammed El HassouniYoussef MourchidMajda LafhelBenjamin Renoustsubject
Theoretical computer scienceComputer science02 engineering and technologyStar (graph theory)[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]computer.software_genre01 natural sciences010305 fluids & plasmas[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Similarity (network science)[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]0103 physical sciences0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]StoryboardDivergence (statistics)Structure (mathematical logic)Network portraitMoviesMultilayer networksNetwork similarity[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]Construct (python library)Scripting languageGraph (abstract data type)020201 artificial intelligence & image processingcomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingdescription
International audience; 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.
year | journal | country | edition | language |
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2020-12-20 |