GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study

arXiv:2605.28643 · cs.CL · Submitted 2026-05-27 · Read on arXiv

cs.CL

Submitted: 2026-05-27

Updated: 2026-08-31

Code: https://github.com/gasmichel/GraphLit

Terminology

• Embeddings — 64× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Heterogeneous Graph — 12× in this paper · explained in 2 episodes
• Ablation — 11× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• Clustering — 6× in this paper · explained in 2 episodes
• Grounding — 5× in this paper · explained in 3 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Character Name Clustering — 4× in this paper
• Self-Supervised Learning — 4× in this paper · explained in 7 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Character Embedding Benchmark — 3× in this paper
• Heterogeneous Graph Transformer — 3× in this paper
• Contextual Understanding — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Multi Layer Perceptron — 2× in this paper
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alternation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Local Character Network — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Mining and modeling character networks
• Heterogeneous Graph Transformer
• Variational Graph Auto-Encoders
• The NarrativeQA Reading Comprehension Challenge
• LongBench Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark
• The Llama 3 Herd of Models
• Computational Representations of Character Significance in Novels
• One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium
• Attention Is All You Need
• Multilingual E5 Text Embeddings: A Technical Report
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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