Scalable Graph Classification via Random Walk Fingerprints (Extended Abstract)
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers. Pages 10912-10915.
https://doi.org/10.24963/ijcai.2025/1215
Keywords
- Sister Conferences Best Papers: Data Mining
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.
@inproceedings{ijcai2025p1215,
title = {Scalable Graph Classification via Random Walk Fingerprints (Extended Abstract)},
author = {Li, Peiyan and Wang, Honglian and Böhm, Christian},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {10912--10915},
year = {2025},
month = {8},
note = {Sister Conferences Best Papers},
doi = {10.24963/ijcai.2025/1215},
url = {https://doi.org/10.24963/ijcai.2025/1215},
}
TY - CPAPER TI - Scalable Graph Classification via Random Walk Fingerprints (Extended Abstract) AU - Li, Peiyan AU - Wang, Honglian AU - Böhm, Christian T2 - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2025 SP - 10912 EP - 10915 DO - 10.24963/ijcai.2025/1215 UR - https://doi.org/10.24963/ijcai.2025/1215 ER -Copied
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