## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Eval** & **GCN** & **GAT** & **SAGE** & **MLP** & **NOSMOG** & **Ours** & **ANOSMOG** \\ \hline \multirow{3}{*}{Cora} & tran & \(81.82\pm 1.26\) & \(81.89\pm 0.86\) & \(79.94\pm 2.43\) & \(59.18\pm 1.60\) & \(80.47\pm 1.39\) & \(82.83\pm 1.25\) & \(\uparrow\) 2.93\% \\  & ind & \(82.01\pm 1.68\) & \(83.07\pm 2.06\) & \(80.96\pm 1.90\) & \(59.44\pm 3.36\) & \(80.19\pm 1.81\) & \(80.89\pm 1.79\) & \(\uparrow\) 0.87\% \\  & prod & \(81.45\) & \(81.62\) & \(79.64\) & \(59.22\) & \(80.57\) & \(80.98\) & \(\uparrow\) 0.51\% \\ \hline \multirow{3}{*}{Citeseer} & tran & \(71.79\pm 1.62\) & \(70.97\pm 1.58\) & \(70.52\pm 1.35\) & \(58.51\pm 1.88\) & \(70.81\pm 2.42\) & \(73.46\pm 1.66\) & \(\uparrow\) 3.74\% \\  & ind & \(71.79\pm 3.71\) & \(71.13\pm 3.21\) & \(68.81\pm 3.89\) & \(59.34\pm 4.61\) & \(71.06\pm 2.53\) & \(72.15\pm 2.28\) & \(\uparrow\) 1.53\% \\  & prod & \(70.52\) & \(70.64\) & \(68.71\) & \(58.49\) & \(66.79\) & \(67.50\) & \(\uparrow\) 1.06\% \\ \hline \multirow{3}{*}{Pubmed} & tran & \(77.04\pm 2.45\) & \(75.48\pm 1.91\) & \(75.37\pm 2.27\) & \(68.39\pm 3.09\) & \(75.30\pm 2.77\) & \(76.88\pm 2.29\) & \(\uparrow\) 2.10\% \\  & ind & \(76.40\pm 2.76\) & \(75.62\pm 2.65\) & \(75.31\pm 2.71\) & \(68.29\pm 3.26\) & \(75.41\pm 2.71\) & \(75.85\pm 3.35\) & \(\uparrow\) 0.58\% \\  & prod & \(76.01\) & \(75.89\) & \(75.02\) & \(68.29\) & \(75.31\) & \(75.84\) & \(\uparrow\) 0.70\% \\ \hline \multirow{3}{*}{A-computer} & tran & \(83.52\pm 1.05\) & \(82.99\pm 1.32\) & \(81.94\pm 1.75\) & \(67.79\pm 2.16\) & \(83.05\pm 1.71\) & \(83.43\pm 1.59\) & \(\uparrow\) 0.46\% \\  & ind & \(82.72\pm 1.91\) & \(83.48\pm 1.19\) & \(82.62\pm 1.53\) & \(67.88\pm 2.15\) & \(83.09\pm 1.99\) & \(83.88\pm 1.64\) & \(\uparrow\) 0.95\% \\  & prod & \(82.77\) & \(82.69\) & \(82.49\) & \(67.70\) & \(82.88\) & \(83.46\) & \(\uparrow\) 0.70\% \\ \hline \multirow{3}{*}{A-photo} & tran & \(91.13\pm 1.31\) & \(91.70\pm 0.86\) & \(91.16\pm 0.48\) & \(77.29\pm 1.79\) & \(92.33\pm 0.53\) & \(92.95\pm 0.82\) & \(\uparrow\) 0.67\% \\  & ind & \(91.74\pm 1.33\) & \(91.79\pm 1.22\) & \(91.18\pm 1.40\) & \(77.44\pm 1.50\) & \(92.52\pm 0.82\) & \(92.39\pm 0.73\) & \(\downarrow\) 0.14\% \\  & prod & \(90.68\) & \(90.89\) & \(91.00\) & \(77.29\) & \(92.45\) & \(92.70\) & \(\uparrow\) 0.27\% \\ \hline \multirow{3}{*}{Arxiv} & tran & - & - & \(70.69\pm 0.24\) & \(55.57\pm 0.36\) & \(70.44\pm 0.31\) & \(70.98\pm 0.20\) & \(\uparrow\) 0.77\% \\  & ind & - & - & \(70.66\pm 0.39\) & \(55.71\pm 0.36\) & \(67.67\pm 0.50\) & \(68.31\pm 0.53\) & \(\uparrow\) 0.95\% \\  & prod & - & - & \(70.48\) & \(55.65\) & \(69.72\pm 0.87\) & \(70.42\pm 0.54\) & \(\uparrow\) 1.00\% \\ \hline \multirow{3}{*}{Products} & tran & - & - & \(77.77\pm 0.19\) & \(59.99\pm 0.11\) & \(77.35\pm 0.30\) & \(77.93\pm 0.22\) & \(\uparrow\) 0.75\% \\  & ind & - & - & \(77.51\pm 0.35\) & \(59.98\pm 0.10\) & \(76.71\pm 0.42\) & \(77.38\pm 0.33\) & \(\uparrow\) 0.87\% \\ \cline{1-1}  & prod & - & - & \(77.42\) & \(59.25\) & \(77.19\) & \(77.82\) & \(\uparrow\) 0.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Node classification results on the seven datasets. fewer soft label outputs from the teacher model. The graph contrastive learning module had a significant impact on model performance, indicating that rich graph structure features can help improve the performance of the student model. Finally, KDGCL achieved improved performance on all datasets, demonstrating its overall effectiveness.