# Mining and modeling character networks

arXiv: https://arxiv.org/abs/1608.00646 · HTML: https://mlsanity.com/paper/1608.00646/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [A City of Millions: Mapping Literary Social Networks At Scale](https://mlsanity.com/paper/2502.19590/) (2025)
- [One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium](https://mlsanity.com/paper/2210.07871/) (2022)
- [Computational Representations of Character Significance in Novels](https://mlsanity.com/paper/2601.15508/) (2026)
- [GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study](https://mlsanity.com/paper/2605.28643/) (2026)

## Abstract (arXiv)

We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of beveridge, we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King's The Stand, and J.K. Rowling's Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http://moviegalaxies.com/ database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we conjecture why this may be the case.