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Balance of thrones: a network study on Game of Thrones

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arxiv 1707.05213 v5 pith:4IT6P42Z submitted 2017-07-17 cs.SI

classification cs.SI
keywords analysisexplorenetworksocialdifferentfeatureshousesinteractions
verification ladder T0 review T1 audit T2 compute T3 formal
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TV dramas constitute an important part of the entertainment industry, with popular shows attracting millions of viewers and resulting in significant revenues. Finding a way to explore formally the social dynamics underpinning these show has therefore important implications, as it would allow us not only to understand which features are most likely to be associated with the popularity of a show, but also to explore the extent to which such fictional world have social interactions comparable with the real world. To begin tackling this question, we employed network analysis to systematically and quantitatively explore how the interactions between noble houses of the fantasy drama TV series Game of Thrones change as the show progresses. Our analysis discloses the invisible threads that connected different houses and shows how tension across the houses, as measure via structural balance, changes over time. To boost the impact of our analysis, we further extended our analysis to explore how different network features correlate with viewers engagement and appreciation of different episodes. This allowed us to derive an hierarchy of features that are associated with the audience response. All in all, our work show how network models may be able to capture social relations present in complex artificial worlds, thus providing a way to qualitatively model social interactions among fictional characters, hence allowing a minimal formal description of the unfolding of stories that can be instrumental in managing complex narratives.

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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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