This paper studies the discovery of approximate rules in property graphs. We propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) at almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. We present a new characterisation of GED satisfaction, and devise a depth-first search strategy to traverse the search space of candidate rules efficiently. Further, we perform experiments to demonstrate the feasibility and scalability of our solution, FASTAGEDS, with three real-world graphs.
@article{arxiv.2304.02323,
title = {FASTAGEDS: Fast Approximate Graph Entity Dependency Discovery},
author = {Guangtong Zhou and Selasi Kwashie and Yidi Zhang and Michael Bewong and Vincent M. Nofong and Debo Cheng and Keqing He and Zaiwen Feng},
journal= {arXiv preprint arXiv:2304.02323},
year = {2023}
}
Comments
7 pages, 5 figures. arXiv admin note: text overlap with arXiv:2301.06264