Multi-Graph Encoder-Decoder Model for Location-Based Character Networks in Literary Narrative

Avi Bleiweiss

2023

Abstract

In the past decade, an extended line of research developed a broad range of methods for reasoning about narrative from a social perspective. This often revolved around transforming literary text into a character network representation. However, there remain inconsistent traits of narrative structure produced computationally by either neural language technology or network theory tools. In this paper, we propose an encoder-decoder model with a main objective to mitigate the apparent computational divergence. Our encoder novelty lies in generating hundreds of location-based network graphs to render a fine-grained narrative. We further formalize a decoder task for detecting character communities and analyze modularity and membership affiliation. Through empirical experiments, we present visualization of stages in our computational process for four literary fiction novels.

Paper Citation

in Harvard Style

Bleiweiss A. (2023). Multi-Graph Encoder-Decoder Model for Location-Based Character Networks in Literary Narrative. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 790-797. DOI: 10.5220/0011776400003393

in Bibtex Style

@conference{icaart23,
author={Avi Bleiweiss},
title={Multi-Graph Encoder-Decoder Model for Location-Based Character Networks in Literary Narrative},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={790-797},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011776400003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Multi-Graph Encoder-Decoder Model for Location-Based Character Networks in Literary Narrative
SN - 978-989-758-623-1
AU - Bleiweiss A.
PY - 2023
SP - 790
EP - 797
DO - 10.5220/0011776400003393