A Graph Neural Network Approach for Hybrid Node-Edge Fault Diagnosis in Interconnection Networks Under the HPMC * Model

Fault diagnosis is crucial for ensuring the reliability of interconnection networks. Traditional diagnostic models usually assume that edges connected to faulty nodes are fault-free, which is unrealistic in practice where both node and edge failures can occur simultaneously. The recently proposed HPMC* diagnostic model provides a more realistic framework by considering both node and edge failures simultaneously, but existing diagnostic approaches under this model have significant limitations in handling complex fault scenarios. This paper proposes HYBRID-GNN, the first graph neural network-based approach for hybrid fault diagnosis under the HPMC* model. HYBRID-GNN employs an edge-enhanced GraphSAGE with comprehensive feature engineering that extracts diagnostic characteristics from HPMC* syndrome data and enables joint training for node and edge fault prediction. HYBRID-GNN learns complex fault patterns from syndrome data, overcoming traditional diagnosability constraints. Experiments on multiple interconnection network topologies show that HYBRID-GNN matches the traditional algorithm in node fault diagnosis (achieving over 99% accuracy within the hybrid diagnosability bound), while delivering substantially higher performance in link fault diagnosis (with accuracy above 97%). Even beyond the diagnosability bound, HYBRID-GNN remains robust, maintaining over 98% node accuracy and over 83% link precision under high fault rates. Furthermore, results on real-world networks further validate its practical effectiveness, achieving over 99% node accuracy and over 95% link accuracy.