# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
## 4 Research Methodology
### Obtaining Node Feature Vectors Based on GCN  
Traditional feature extraction methods only focus on the local neighborhood information of the nodes and cannot effectively capture the information in the global graph structure, and for large-scale graphs, traditional feature extraction methods increase the computational and storage costs intending to extract high-dimensional feature vectors, which reduces the generalization ability of the model. To avoid the above problems, GCNs are used to extract data features. The use of a GCN can fully utilize the provided graph structure information to obtain the feature representation of each node through inter-node information transfer, and residual connections are added to the GCN to solve the gradient propagation problem and maintain the integrity of information in deep networks. In the data processing section, the hypergraph correlation matrix constructed in Section 4.1 is transformed into node features, and the GCN is used as the model for training during the transformation process. During the entire data processing process, the GCN first constructs the input hypergraph correlation matrix into a hypergraph structure; then, the node features are initialized. Next, the node features are multiplied with the hypergraph association matrix using the hypergraph convolution operation combined with a learnable hypergraph convolution kernel to capture the complex relationship between nodes and hyperedges and generate the updated node features. Subsequently, a nonlinear transformation is introduced via a nonlinear activation function to enhance the model representation. The updated node features are then aggregated to obtain a higher-level representation of the nodes. These steps are iterated several times to propagate the information throughout the network. Finally, the iterated node features are input to the output layer for link prediction task processing. The whole process completes the extraction of node features and task processing by optimizing the cross-entropy loss function and continuously adjusting the model parameters to minimize the difference between the predicted values and the real labels. Figure 4 describes the process of feature extraction by the GCN. Adopting the GCN effectively utilizes the graph structure information to realize the information transfer between nodes and obtains the feature representation of each node through inter-node information transfer, which enables the model to better capture the local and global information of the graph data and improves its ability to characterize the node features and the performance of the model in terms of representing the node's feature vectors well. Meanwhile, since graph data usually have a complex structure and a large number of nodes, the problem of gradient vanishing occurs in the deep network as the network depth increases. Therefore, in the above GCN process of obtaining node feature vectors, the residual connection is realized by adding the output of the first hidden layer with the input of the second layer so that the gradient can be directly propagated back to the earliest layer of the network through the residual connection, which, in turn, improves the training efficiency and performance of this model. The addition of residual connections also maintains the integrity of the input features and reduces the loss of information in the inter-layer transfer process. The residual \(\hat{\hat{\epsilon}}\), which is the gap between the model output and the actual output, is shown in Equation (12), where \(y\) is the output of the GCN model and \(\hat{y}\) is the desired output. \[\hat{\hat{\epsilon}}=y-\hat{\hat{y}} \tag{12}\]  
In order to better capture the relationship between nodes and improve the representation ability of the model, a double-headed attention mechanism is added. It dynamically adjusts the importance between nodes by learning the attention coefficient and aggregates  
Figure 4: Obtaining node feature vectors by the GCN.