# Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification
## 3 Results
### Experimental Parameters
#### 3.2.1 Transductive Learning  
All models' hidden layers are 2 and each channel of feature dimensions are shown in these tables. In the experimental parameter Table 2. This experiment is formulated by setting the characteristic number to 8, 16, 32, 64 and the channel number of all models to 8. In the experiment, GAT [12] is used as the baseline, GAT-A-FX as the control model, and our original model Geometric-V-Sub, is compared to carry out the accuracy experiment. Table 3, respectively, records the number of parameters on Cora, Citeseer and PubMed data sets when each channel of GAT, GAT-A-FX and Geometric-V-Sub contains 8, 16, 32 and 64 features. From Tables 2 and 3, it can be seen that the accuracy of GAT-A-FX has a small change compared with GAT [12] under the same feature number input, while the accuracy of Geometric-V-Sub has decreased and improved compared with GAT under the same feature number input. Based on the GAT-A-FX network structure model, the Cora dataset has a decrease of 0.4% (16 features) to 0.2% (64 features). On the Citeseer dataset, there is a 1% increase (8 features) to a 1.4% decrease (16 features). There was a 0.4% decrease on the PubMed dataset (8, 16, 64 features). Although there was a small amount of parameter improvement when the attention vector width was increased, there was no performance improvement in the corresponding experimental group, and even a partial decrease in accuracy. Therefore, it proved that the transformation of GAT relatively failed in the improvement direction of only increasing the attention vector width and inserting activation functions to improve generalization ability in the transductive learning task. The Geometric-V-Sub network structure model has a decrease of 7% (8 features) to 2% (32 features) on the Cora dataset, but the Geometric-V-Sub network structure model has a decrease of 12.75% (8 features, Geometric-V-Sub is 12,245 and GAT is 92,302) to 13.95% (64 features, Geometric-V-Sub is 103,021 and GAT is 738,318) on the parameters of the control model GAT. There is a 9% (8 features) to 5.6% decrease in the Citeseer dataset, but the parameters of the Geometric-V-Sub network structure model are 13.27% (8 features, Geometric-V-Sub is 30,300 and GAT is 237,516) to 13.05% (64 features, Geometric-V-Sub is 247,972 and GAT is 1,900,044) of the control model GAT. On the PubMed dataset, there is a decrease of 0.4% (8 features) and an increase of 0.6% (16, 64 features). Based on the Geometric-V-Sub network structure model, the the number of parameters is 13.68% (8 features, Geometric-V-Sub is 4421 and GAT is 32,326) to 16.26% (64 features, Geometric-V-Sub is 42,053 and GAT is 258,566) of the control model GAT. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Hidden Layer** & **Cora (\%)** & **Citeseer (\%)** & **Pubmed (\%)** \\ \hline \multirow{4}{*}{GAT} & 8 & 82 & 74 & 81.2 \\  & 16 & 81.4 & 74.2 & 81.2 \\  & 32 & 82.4 & 72.6 & 80.8 \\  & 64 & 82.4 & 74 & 81.2 \\ \hline \multirow{4}{*}{GAT-A-FX} & 8 & 82 & 75 & 80.8 \\  & 16 & 81.8 & 72.8 & 80.8 \\  & 32 & 82.4 & 72.6 & 80.8 \\  & 64 & 82.2 & 72.8 & 80.8 \\ \hline \multirow{4}{*}{Geometric-V-Sub} & 8 & 75 & 65 & 80.8 \\  & 16 & 78.8 & 67.4 & 81.8 \\ \cline{1-1}  & 32 & 80.4 & 67 & 81.2 \\ \cline{1-1}  & 64 & 79.6 & 68 & 81.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy comparison in transductive learning. It can be seen from this result that when the number of parameters is reduced too much, it will have a great impact on the dataset with a large feature dimension of the input node (for example, the feature dimension of Citeseer node is 3703, while the feature dimension of PubMed node is only 500). In this experiment, the counts of parameters of Geometric-V-Sub in the first layer is only GAT 1/8 in the node feature transformation, so the dataset with larger node feature dimensions performs poorly. For the dataset with fewer node features, the node feature transformation can be fully mapped and less information is lost, so better results can be obtained.