Using GraphSAGE to Track the Transmission Path of Salmonella in the Dairy Supply Chain

Due to the complexity of the current dairy supply chain and isolated data, traditional traceability methods are inefficient and cross-link transmission paths are difficult to identify when Salmonella breaks out. The purpose of this paper is to build a graph neural network model based on GraphSAGE to accurately locate the source of contamination and the transmission path. First, a heterogeneous supply chain graph is constructed: this paper takes dairy farms, processing plants, logistics vehicles, supermarkets, etc., as nodes, and the relationship between goods flow and personnel contact as edges; secondly, feature engineering is performed: nodes are embedded with Salmonella gene sequence data (SNP clustering), environmental temperature and humidity monitoring data, and enterprise quality inspection records. This paper trains GraphSAGE: a two-layer aggregation function (mean aggregator) is used, the number of sampled neighbors k=15, and the node representation is optimized after 20 rounds of iterations. It calculates the probability of cross-link transmission through node embedding and outputs high infection risk paths. In three real epidemic cases, the model traceability accuracy rate reached 94.7%, the recall rate was 92.1%. GraphSAGE significantly improves the efficiency of pathogen transmission path tracking in complex supply chains by integrating multi-source heterogeneous data and graph structure learning, providing decision support for rapid epidemic prevention.