# Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification
## 3 Results
### Experimental Parameters
#### 3.2.2 Inductive Learning  
The inductive learning experiment was carried out on PPI dataset. The number of channels for GAT, GAT-A-FX and Geometric-V-Sub models is specified as 8. The input feature number of each model is set to 32, 64, 128 and 256, respectively. Table 4 records the number of parameters required by the 3 models under different characteristic numbers of each channel and F1-Score of the each model.  
It can be seen from Table 4 that GAT-A-FX is 6.5 lower than GAT F1 score (32 feature numbers, GAT-A-FX the number of parameters is 111,844, GAT the number of parameters is 110,578) to 2.1 (256 feature numbers, GAT-A-FX the number of parameters is 4,561,380, GAT  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hidden Layer** & \multirow{2}{*}{**F1-Score**} & **Total** \\  & **Dimension** & & **Parameters** \\ \hline \multirow{4}{*}{GAT} & 32 & 91.8 & 110,578 \\  & 64 & 97.2 & 351,986 \\  & 128 & 98.4 & 1,228,018 \\  & 256 & 98.6 & 4,552,946 \\ \hline \multirow{4}{*}{GAT-A-FX} & 32 & 85.3 & 111,844 \\  & 64 & 93.7 & 354,276 \\ \cline{1-1}  & 128 & 95.9 & 1,232,356 \\ \cline{1-1}  & 256 & 96.5 & 4,561,380 \\ \hline \multirow{4}{*}{Geometric-V-Sub} & 32 & 83.9 & 97,405 \\ \cline{1-1}  & 64 & 91.9 & 130,877 \\ \cline{1-1}  & 128 & 96.7 & 228,541 \\ \cline{1-1}  & 256 & 98.4 & 546,749 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison and parameter comparison in inductive learning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Hidden Layer** & \multirow{2}{*}{**Cora**} & **Citeseer** & **Pubmed** \\  & **Dimension** & & & \\ \hline \multirow{4}{*}{GAT} & 8 & 92,302 & 237,516 & 32,326 \\  & 16 & 184,590 & 475,020 & 64,646 \\  & 32 & 369,692 & 950,552 & 129,804 \\  & 64 & 738,318 & 1,900,044 & 258,566 \\ \hline \multirow{4}{*}{GAT-A-FX} & 8 & 92,44  
the number of parameters is 4,552,946). Although there was a small increase in parameters when increasing the width of attention vector, there was no due performance improvement in the corresponding experimental group, and even a small decrease in F1 score. Therefore, it was proved that the transformation of GAT was relatively failed in the improvement direction of only increasing the width of attention vector and inserting activation function to improve generalization ability in the task of inductive learning. Geometric-v-sub is 7.9 lower than GAT F1-Score (32 feature numbers, GAT-A-FX the number of parameters is 97,405, GAT the number of parameters is 110,578, and the parameter is 88.09% of baseline) to 0.2 (256 feature numbers, GAT-A-FX the number of parameters is 546,749, GAT the number of parameters is 4,552,946, and the parameter is 12.01% of baseline). After greatly reducing the number of parameters, Geometric-V-Sub can still get F1 score close to the near baseline, which shows that a large number of parameters are actually wasted on the original GAT structure to a certain extent. Therefore, it is necessary to lighten the weight. It is necessary to study how to reasonably allocate parameters to maximize the parameter efficiency of the model, and as the edge of information transmission, it can well transmit information with the support of geometric features, it is helpful for better node classification.