\begin{table*}[h]
\setlength\tabcolsep{12pt}
\centering
\small
\caption{AUC-ROC on ogbn-proteins and accuracy on ogbn-products of the best graph SSL methods. We consider GCN and GraphSage as the backbone model for each method.}
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\begin{tabular}{ll|ll}
\toprule
\bfseries{Paradigm} & \bfseries{Method}                        & \bfseries{ogbn-proteins}                & \bfseries{ogbn-products}                \\
\midrule
\multirow{2}{*}{Supervised}        & GCN                           & 72.51 $\pm$ 0.01             & 75.64 $\pm$ 0.01             \\
                  & GraphSage                     & 77.68 $\pm$ 0.01             & 78.29 $\pm$ 0.01             \\
\midrule
\multirow{7}{*}{Self-Supervised}   & InfoGCL                       & OOM                          & OOM                          \\
                  & GraphMAE                      & 62.52 $\pm$ 0.69             & 72.88 $\pm$ 0.37             \\
                  & GRACE                         & 68.40 $\pm$ 0.59             & 71.55 $\pm$ 0.88             \\
                  & LaGraph                       & 71.86 $\pm$ 0.28             & 73.23 $\pm$ 0.25             \\
                  & BGRL                          & \underline{73.25 $\pm$ 0.79} & 72.86 $\pm$ 0.64             \\
                  & CCA-SSG                       & 73.08 $\pm$ 0.37             & \underline{73.46 $\pm$ 0.26} \\
                  & \method (ours) & \textbf{77.80 $\pm$ 0.21}        & \textbf{76.50 $\pm$ 0.18} \\
\bottomrule
\end{tabular}
\vspace{0.5cm}
\label{tab:acc_large}
\end{table*}

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