融合路径优化的异构图神经网络算法

Heterogeneous Graph Neural Network Algorithm for Fusion Path Optimization

图神经网络(GNNs)因其在图数据分析方面的卓越性能而受到广泛关注。异构图神经网络(HGNNs)通过处理含有多种类型节点和边的异构图数据,为解决复杂数据分析问题提供了新视角。为提升异构图数据处理准确性与效率,本研究提出了一种融合路径优化的异构图神经网络算法。算法采用特征传播模块、节点中心性编码、相似度编码、路径优化聚合等技术,旨在通过优化特征传播路径与提高节点信息编码的有效性,强化异构图神经网络对复杂图数据的处理能力。通过公开数据集对提出算法进行算法性能验证,结果表明,通过融合路径优化技术,所提算法在多项性能指标上均显著优于现有异构图神经网络模型,在数据处理效率和模型准确性方面展现出了优异的性能,研究成果可为复杂异构图数据的高效处理提供新的解决方案。

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.