# Improving Graph Neural Network Models in Link Prediction Task via A Policy-Based Training Method
## 4 Experiments
### Effectiveness of the PbTRM  
We conduct the experiment with the GCN, GraphSAGE, and GAT models on the datasets, including Cora and CiteSeer. The datasets are split following Table 1. To verify the effectiveness of the proposed PbTRM, we train the GNN models with two different methods. The GNN models trained with the proposed PbTRM, denoted as GNN+PbTRM, generate negative samples in the training sets by using the negative sample selector following Algorithm 1. The GNN models, which generate the negative samples in the training set by randomly sampling unobserved edges, are used as benchmarking GNN models. For a fair comparison, the \(T\) values of a sampling period for the GNN+PbTRM and benchmarking GNN models are all set as 1. When \(T\) value is set to 1, the GNN+PbTRM and benchmarking GNN models update the negative samples in their training sets in each training epoch. The prediction performance of the benchmarking GNN models is used as the baseline performance. The prediction performance is evaluated by two standard metrics in the link prediction task, including AUC [5] and AP [5], and the results are listed in Tables 2 and 3.  
From Tables 2 and 3, it can be observed that the AUC and AP values of the GCN, GAT, and GraphSAGE models trained with the proposed PbTRM are all higher than those of corresponding benchmarking GNN models. In comparison to their benchmarking GNN  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cora** & \\ \hline
**Method** & **AUC** & **AP** \\ \hline GCN & 0.8887 & 0.9051 \\ GCN+PbTRM & **0.8988** & **0.9149** \\ \hline GAT & 0.8527 & 0.8851 \\ GAT+PbTRM & **0.8618** & **0.8906** \\ \hline GraphSAGE & 0.8298 & 0.8221 \\ GraphSAGE+PbTRM & **0.8457** & **0.8538** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of prediction performance between GNN+PbTRM and benchmarking GNN models on Cora.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **CiteSeer** & \\ \hline
**Method** & **AUC** & **AP** \\ \hline GCN & 0.8905 & 0.9065 \\ GCN+PbTRM & **0.9011** & **0.9149** \\ \hline GAT & 0.8295 & 0.8619 \\ GAT+PbTRM & **0.8447** & **0.8699** \\ \hline GraphSAGE & 0.7711 & 0.7717 \\ GraphSAGE+PbTRM & **0.7989** & **0.8030** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of prediction performance between GNN+PbTRM and benchmarking GNN models on CiteSeer.  
models, the GNN+PbTRM models employ the negative sample selector to select high-quality negative samples from the randomly sampled unobserved edges. The parameters of the policy network are updated at the end of each sampling period, which promotes the negative sample selector to perform better. The augmented AUC and AP values indicate that the negative samples generated by the negative sample selector can make the GNN models outperform their baseline performance, which demonstrates the effectiveness of the proposed PbTRM.  
In this experiment, we also observed that the time required by the training of the GNN+PbTRM models is longer than that of the benchmarking GNN methods. In the proposed PbTRM, it is necessary for the randomly sampled unobserved edges to be selected by the negative sample selector, and then the parameters of the policy network need to be updated in each sampling period. While the aforementioned two steps increase the time required by the training of the GNN+PbTRM models, they enhance the prediction performance of the GNN+PbTRM models. This shows that although the time consumption of the proposed PbTRM is longer than that of the random sampling of the unobserved edges, it is necessary for the effectiveness of the proposed PbTRM.