Multi-Source Shortest Paths Mining Algorithm for Unweighted Directed Graphs Based on Rough Sets Theory

Shortest path mining is one of the classic problems in graph theory. How to design an efficient multi-source shortest paths mining algorithm has practical significance. In this article, an efficient multi-source shortest paths algorithm is designed based on rough sets theory. Firstly, within the framework of rough sets theory, investigation has been conducted on the multisource shortest paths, providing a theoretical basis for calculating multi-source shortest paths. Secondly, the R-related set operator is used to search all vertices reachable from each source vertex. Meanwhile, the k-step upper approximation operator is used to search for all vertices that can reach the target vertex. Then, a marking strategy is designed to control the construction of multi-source shortest paths, which can improve the mining efficiency. Finally, a multi-source shortest paths algorithm for unweighted directed graphs (RMSSP) based on rough sets theory is proposed. Experimental results show that the RMSSP algorithm can accurately discover multi-source shortest paths for unweighted directed graphs. Also, it performs better than similar algorithms in terms of computational efficiency. With the expansion of the dataset size, the efficiency advantage of RMSSP algorithm is more obvious.