---
title: "Scalable Graph Classification via Random Walk Fingerprints (Extended Abstract)"
authors:
  - "Peiyan Li"
  - "Honglian Wang"
  - "Christian Böhm"
doi: "10.24963/ijcai.2025/1215"
url: "https://ijcai.org/proceedings/2025/1215"
pdf: "https://ijcai.org/proceedings/2025/1215.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10912-10915"
track: "Sister Conferences Best Papers"
keywords:
  - "Sister Conferences Best Papers: Data Mining"
---

# Scalable Graph Classification via Random Walk Fingerprints (Extended Abstract)

Peiyan Li, Honglian Wang, Christian Böhm

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10912-10915. DOI: [10.24963/ijcai.2025/1215](https://doi.org/10.24963/ijcai.2025/1215)

## Abstract

We design a lightweight structural feature extraction technique for graph classification. It leverages node subsets and connection strength reflected by random-walk-based heuristics, presenting a scalable, unsupervised, and easily interpretable alternative. We provide theoretical insights into our technical design and establish a relation between the extracted structural features and the graph spectrum. We show our method achieves high levels of computational efficiency while maintaining robust classification accuracy.