Implementation Analysis of Reachability Graph and Its Application in Net Learning

Reachability represents the most fundamental dynamic property of Petri nets. The reachability graph serves as a crucial tool for analyzing reachability, and its generation algorithm has garnered widespread attention. However, most of the existing work has focused primarily on the accuracy of the generation algorithm, often overlooking computational and time costs. Therefore, this paper concentrates on the implementation of reachability graphs and proposes three implementation methods: definition-based (DB), matrix-based (MB), and a fast generation method without self-loops (FG). Through comprehensive experiments, we evaluate and analyze the running speed and applicable scenarios of these three methods. Furthermore, we have successfully applied the proposed methods in net learning.