# FASTAGEDS: Fast Approximate Graph Entity Dependency Discovery

arXiv: https://arxiv.org/abs/2304.02323 · HTML: https://mlsanity.com/paper/2304.02323/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2023.

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

- [Extending Desbordante with Probabilistic Functional Dependency Discovery Support](https://mlsanity.com/paper/2607.23636/) (2026)

## Abstract (arXiv)

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.