FASTAGEDS: Fast Approximate Graph Entity Dependency Discovery
Guang-Tong Zhou, Selasi Kwashie, Yidi Zhang, Michael Bewong, Vincent M. Nofong, Debo Cheng, Keqing He, Zaiwen Feng · arXiv (Cornell University) · 2023
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.