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Network Science Predicts Who Dies Next in Game of Thrones

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arxiv 2110.09856 v1 pith:6QLVZCOA submitted 2021-10-19 cs.SI physics.soc-ph

Milan Janosov This is my paper · ORCID
classification cs.SIphysics.soc-ph
keywords networkgamesciencethronesanalysisapplicationsattentionbook
verification ladder T0 review T1 audit T2 compute T3 formal
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Social network analysis and machine learning have found countless applications in recent years. As an example, this short project was carried out in 2017 and was followed by some media attention, with the following goal: to bring network science and predictive modeling together on the subject of the popular TV and book series, Game of Thrones, and predict which key characters are likely to meet their ends.

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Cited by 1 Pith paper

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  1. Narrative Memory in Machines: Multi-Agent Arc Extraction in Serialized TV

    cs.MM 2025-08 conditional novelty 4.0 of 10

    A multi-agent LLM system with a vector database extracts narrative arcs from TV episode summaries, scoring 89% precision on self-contained arcs while missing overlapping relationship arcs.

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