# GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification
## 5 Experiments and Discussion
### Result Analysis  
The classification results of these baselines in origin papers, which compare with the results of GRNN and GRNN-0 (the benchmark neural network), are summarized in Tables 2-8, where the highest accuracy in each column is highlighted in bold, and the second-highest values are underlined. Our methods are displayed at the bottom half of each table. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Cora** & **Citeseer** & **PubMed** \\ \hline MLP & 55.1 & 46.5 & 71.4 \\ DeepWalk [39] & 67.2 & 43.2 & 65.3 \\ Chebyshev [26] & 81.2 & 69.8 & 74.4 \\ GCN [11] & 81.5 & 70.3 & 79.0 \\ GAT [12] & 83.0 & 72.5 & 79.0 \\ SGC [44] & 81.0 & 71.9 & 78.9 \\ GMNN [18] & 83.7 & 72.9 & **81.8** \\ DAGNN [46] & 84.4 & 73.3 & 80.5 \\ GCNII [45] & 85.5 & 73.4 & 80.2 \\ AGSN [40] & 83.8 & 74.5 & 79.9 \\ FGANN [41] & 83.2 & 72.4 & 79.2 \\ RGCCN [42] & 82.0 & 73.2 & - \\ M-GWNN [47] & 84.6 & 72.6 & 80.4 \\ LC-GAT [43] & 83.5 & 73.8 & 79.1 \\ H-GCN [7] & 84.5 & 72.8 & 79.8 \\ rGNN [48] & 83.89 & 73.35 & - \\ SPC-CNN [32] & 84.8 & 74.0 & 81.0 \\ \hline GRNN-0 & 84.0 \(\pm\) 0.7 & 71.0 \(\pm\) 0.8 & 79.2 \(\pm\) 0.5 \\ GRNN & **86.0 \(\pm\) 0.4** & 74.5 \(\pm\) 0.6 & 81.5 \(\pm\) 0.5 \\ GCN+GRNN & 81.5 \(\pm\) 0.6 & **75.2 \(\pm\) 0.2** & 79.3 \(\pm\) 0.1 \\ GCNII+GRNN & 85.8 \(\pm\) 0.6 & 73.9 \(\pm\) 0.5 & 81.4 \(\pm\) 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance comparison of GRNN with existing graph neural network methods (%). \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **PPI** \\ \hline Random & 39.6 \\ MLP & 42.2 \\ GraphSAGE-GCN [13] & 50.0 \\ GraphSAGE-mean [13] & 59.8 \\ GraphSAGE-LSTM [13] & 61.2 \\ GraphSAGE-pool [13] & 60.0 \\ GAT [12] & 97.3 \\ \hline GRNN-0 & **98.34 \(\pm\) 0.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of micro-averaged F1 scores (%) on PPI. Analyzing Tables 2 and 3, the results demonstrate that GRNN achieved superior performance on all four datasets. Specifically, GRNN achieved the best performance on Cora and the second-best performance on Citeseer and PubMed. GRNN improved on GRNN-0 by a margin of 2.0%, 3.5%, and 2.3% on Cora, Citeseer, and PubMed, respectively. Moreover, GCN+GRNN improved on GCN by a margin of 4.9% on Citeseer, and GCNII+GRNN improved on GCNII by a margin of 1.2% on PubMed, which indicates that it is beneficial to reduce the global smoothing error over the graph by iteratively training the graph neural network. Furthermore, the result of the GRNN-0 model was 1.0% higher than that of the GAT model on PPI, though the GRNN-0 model was the benchmark neural network of the GRNN model. Analyzing Tables 4-6, the results verify the effectiveness of our GRNN model, consistently outperforming other state-of-the-art approaches by a large margin on a wide range \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Label Rate**} \\  & **0.03\%** & **0.05\%** & **0.1\%** & **0.3\%** \\ \hline LP & 61.4 & 66.4 & 65.4 & 66.8 \\ GCN & 60.5 & 57.5 & 65.9 & 77.8 \\ Co-training [6] & 62.2 & 68.3 & 72.7 & 78.2 \\ Self-training [6] & 51.9 & 58.7 & 66.8 & 77.0 \\ Union [6] & 58.4 & 64.0 & 70.7 & 79.2 \\ InterSection [6] & 52.0 & 59.3 & 69.4 & 77.6 \\ MultiStage [50] & 57.4 & 64.3 & 70.2 & - \\ M3S [50] & 59.2 & 64.4 & 70.6 & - \\ \hline GRNN-0 & 60.7 & 61.9 & 73.4 & 78.9 \\ GRNN & **65.3** & **68.8** & **77.3** & **81.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of classification accuracy (%) results with fewer training samples on PubMed.