Heterogeneous Graph Neural Network Algorithm for Fusion Path Optimization
Graph neural networks(GNNs)have received widespread attention due to their excellent performance in graph data analysis.Heterogeneous Graph Neural Networks(HGNNs)provide a new perspective for solving complex data analysis problems by processing heterogeneous graph data containing multiple types of nodes and edges.To improve the accuracy and efficiency of heterogeneous graph data processing,this study proposes a heterogeneous graph neural network algorithm that integrates path optimization.By using techniques such as feature propagation module,node centrality encoding,similarity encoding,and path optimization aggregation,the algorithm aims to enhance the processing ability of heterogeneous graph neural networks for complex graph data by optimizing feature propagation paths and improving the effectiveness of node information encoding.And the proposed algorithm was validated for algorithm performance through publicly available datasets,the results showed that through the fusion path optimization technology,the proposed algorithm significantly surpassed existing heterogeneous graph neural network models in multiple performance indicators,demonstrating excellent performance in data processing efficiency and model accuracy.The research results can provide a new solution for efficient processing of complex heterogeneous graph data.
万方数据
维普
