# Heterogeneous Graph Importance Scoring and Clustering with Automated LLM-based Interpretation

arXiv: https://arxiv.org/abs/2605.02919 · HTML: https://mlsanity.com/paper/2605.02919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Building a Large Japanese Web Corpus for Large Language Models](https://mlsanity.com/paper/2404.17733/) (2024)

## Built on by (most cited first, 1 of 1)

- [Fine-Tuning Vision-Language Models for Understanding Current Damage and Scoring Priority with Quality Guard Agent](https://mlsanity.com/paper/2605.27452/) (2026)

## Abstract (arXiv)

Urban bridge networks are critical infrastructure whose disruption can cascade into severe impacts on transportation, emergency services, and economic activity. This paper presents a comprehensive methodology for assessing bridge importance through heterogeneous graph analysis, unsupervised clustering, and automated interpretation via large language models (LLMs). Our approach addresses three fundamental challenges: (1) quantifying multi-dimensional bridge importance using only open data sources, (2) discovering functional bridge archetypes across different cities, and (3) generating policy-relevant interpretations automatically. We construct heterogeneous graphs from OpenStreetMap (OSM) data incorporating bridges, road networks, buildings, and public facilities. Five social impact indicators are computed: transit desert score, hospital access score, isolation risk score, supply chain impact score, and green space access score. These 52-dimensional feature vectors undergo dimensionality reduction via UMAP and density-based clustering via HDBSCAN. Discovered clusters are interpreted using temperature-optimized LLMs (Elyza8b, trained on construction domain corpus). (1) A complete open-data pipeline from OSM to actionable bridge importance rankings, (2) a five-indicator scoring methodology with 40× computational optimization, (3) a UMAP+HDBSCAN clustering framework validated on multi-city data, (4) an LLM interpretation methodology including temperature optimization and model selection rationale, and (5) transferability demonstration across cities via configuration-only adaptation.