# Movienet: A Movie Multilayer Network Model using Visual and Textual Semantic Cues

arXiv: https://arxiv.org/abs/1910.09368 · HTML: https://mlsanity.com/paper/1910.09368/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation](https://mlsanity.com/paper/2401.06150/) (2023)
- [A Framework for Building Point Cloud Cleaning, Plane Detection and Semantic Segmentation](https://mlsanity.com/paper/2402.00692/) (2024)
- [Les Mis'erables Dialogue: A film-based network representation of social communication](https://mlsanity.com/paper/2610.13148/) (2026)

## Abstract (arXiv)

Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters' interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.