Key node identification method based on graph neural network

The key node identification method based on graph neural network (GNN) is a new network analysis technology, which aims to identify the key nodes in complex networks by deep learning means. In this method, the network structure is transformed into graph data format, and the graph convolution operation is used to update the node representation by aggregating the information of neighbor nodes, so as to enhance the understanding of the dependency relationship between nodes. On this basis, the algorithm extracts node feature representation by multi-layer perceptron, which is used to extract high-level abstract features from node features, and enhances the learning ability of the model for complex patterns. In order to improve the accuracy of key node identification, the attention mechanism is combined to optimize the weight distribution of nodes, so that important nodes get higher weight in the final score. Finally, the unsupervised learning or semi-supervised learning strategy is used to train the model, which automatically learns the network structure characteristics and co-trains the model to adapt to the needs of different scenarios. The experimental results show that the GNN-based method is significantly better than the traditional method in terms of accuracy and efficiency, and provides a powerful tool for network analysis.