Hybrid intermittent fault diagnosis of general graphs

Abstract

With the rapid development of information technology, networks have emerged as a crucial infrastructure in the big data era. System-level fault diagnosis plays a vital role to locate and repair faulty nodes in networks. However, the majority of research primarily focus on diagnosing faulty nodes of regular networks, with comparably less attention devoted to fault identification in irregular networks under the circumstance of link failures. In this paper, we introduce the notion of hybrid intermittent fault diagnosability and derive the corresponding diagnosability for general networks. Additionally, we determine the hybrid intermittent fault diagnosability for various well-known networks. Furthermore, we propose a HIFPD-MM* algorithm, which possesses a time complexity of $O(k\times \left|V\left(G\right)\right|\cdot {\left(\Delta \left(G\right)\right)}^2)$, where $k$ denotes the number of stages of the algorithm in one round, and $\Delta \left(G\right)$ denotes the maximum degree of graph $G$. Through extensive experiments conducted on hypercubes and real-world datasets, we validate the effectiveness and accuracy of our proposed algorithm.