# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
## 5 Experimentation
### Experimental Results  
In order to verify the effectiveness of the combination of the HWA algorithm and the GCN model proposed in the previous section, link prediction was carried out in this study using the eight real network datasets mentioned in Section 5.2 as an example. Table 3 lists the prediction results of the prediction model proposed in this paper on the eight datasets. By analyzing the data in the following table in detail, it can be seen that the model proposed in this paper showed excellent performance on the eight real network datasets mentioned above. These results strongly prove the validity and reliability of the model proposed in this experiment. In this experiment, three advanced GNN-based methods were selected as benchmarks. They are a variant of the graph neural network GIN [32], the hypergraph neural network framework HGNN [33; 34] and the hypergraph neural network HyperGCN [34]. Tables 4 and 5 show the results of the AUC and recall obtained by the model in this experiment compared with the other algorithms. Through comparison, it was found that the performance of the model in this experiment was almost the best in terms of link prediction. Table 4 shows the AUC indicators of the model proposed in this paper and of the other baseline models under the different test sets. From the data in the table, it can be concluded that the experimental model proposed in this paper almost had the best performance in terms of link prediction. For example, in the Bio-CE-GT real network data, the AUC index of the model proposed in this paper was 7.49%, 1.01% and 1.82% higher than that of the three baseline models. However, it can be found from the figure that in the PB real network data, the HGNN model was not much different from the model proposed in this experiment. The main reason for this situation is that hypergraph neural networks are trained directly on hypergraphs, so the structural characteristics of the hypergraphs directly affect the high-order structural information extracted by the network. From this point of view, the model proposed in this experiment was not always the most effective, but the overall effect was the best, so it can be confirmed that the model proposed in this experiment is effective. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Bio-CE-GT** & **Celegans** & **Ecoli** & **Facebook** & **Jazz** & **Metabolic** & **PB** & **Soc-wiki-vote** \\ \hline GIN & 0.6081 & 0.5884 & 0.5970 & 0.6497 & 0.6639 & 0.6273 & 0.7455 & 0.5759 \\ HGNN & 0.6729 & 0.6419 & 0.6748 & 0.7273 & 0.7091 & 0.6974 & 0.7827 & 0.6386 \\ HyperGCN & 0.6648 & 0.6461 & 0.6708 & 0.6810 & 0.7439 & 0.6825 & 0.7376 & 0.6225 \\ Ours & 0.6830 & 0.6521 & 0.7006 & 0.7941 & 0.7793 & 0.7040 & 0.7842 & 0.6410 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of AUC comparison between this experimental model and the four baseline models for link prediction. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Bio-CE-GT** & **Celegans** & **Ecoli** & **Facebook** & **Jazz** & **Metabolic** & **PB** & **Soc-wiki-vote** \\ \hline AUC & 0.6830 & 0.6521 & 0.7006 & 0.7941 & 0.7793 & 0.7040 & 0.7842 & 0.6410 \\ F1 & 0.6788 & 0.6515 & 0.6903 & 0.7940 & 0.7892 & 0.6978 & 0.7838 & 0.6300 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AUC and F1 values for link prediction by the model proposed in this experiment. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **Bio-CE-GT** & **Celegans** & **Ecoli** & **Facebook** & **Jazz** & **Metabolic** & **PB** & **Soc-wiki-vote** \\ \hline GIN & 0.6024 & 0.5856 & 0.5905 & 0.6459 & 0.6632 & 0.6215 & 0.7438 & 0.5713 \\ HGNN & 0.6597 & 0.6324 & 0.6614 & 0.7237 & 0.7053 & 0.6901 & 0.7815 & 0.6208 \\ HyperGCN & 0.6530 & 0.6404 & 0.6569 & 0.6747 & 0.7341 & 0.6735 & 0.7356 & 0.6072 \\ Ours & 0.6788 & 0.6515 & 0.6903 & 0.7940 & 0.7892 & 0.6978 & 0.7838 & 0.6300 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of F1 values for link prediction by this experimental model and four baseline models.