# Fair Benchmark for Unsupervised Node Representation Learning
## 4 Results
### Overall Rank of Node Representation Learning Methods for Pure Networks  
The overall ranks of unsupervised NRL methods for pure networks are summarized in Figure 3, which are computed based on Equation (2). Node attributes are not used in this evaluation. Their results in Tasks 1, 2, and 3, are summarized in Tables 3-5.  
Figure 3: Overall rank of unsupervised NRL algorithms for pure networks, evaluated by FURL. We visualize the ranks of each selected pure network embedding algorithm using a bar chart. The algorithms are sorted by their overall ranks in ascending order, from left to right.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cona**} & **Fickick** & **BigCatalog** & **Citeseer** & **Pubmed** & **Chameleon** & **Finn** & **Squired** \\ \hline Macronym & CAR & 0.8061 \(\pm\)0.0195 & 0.4458 \(\pm\) 0.0114 & 0.7012 \(\pm\) 0.0136 & 0.6793 \(\pm\) 0.0143 & 0.8016 \(\pm\) 0.0099 & 0.5906 \(\pm\) 0.0203 & 0.2080 \(\pm\) 0.0014 & 0.3868 \(\pm\) 0.0138 \\  & CAN & **0.8668** \(\pm\) 0.0173 & 0.7728 \(\pm\) 0.0005 & 0.8037 \(\pm\) 0.0103 & **0.7112** \(\pm\) 0.0146 & 0.8303 \(\pm\) 0.0008 & **0.6538** \(\pm\) 0.0213 & 0.2699 \(\pm\) 0.0002 & **0.5909** \(\pm\) 0.0156 \\  & DGI & 0.8376 \(\pm\) 0.0155 & 0.4520 \(\pm\) 0.0022 & 0.7348 \(\pm\) 0.0116 & 0.7411 \(\pm\) 0.0215 & **0.8628** \(\pm\) 0.0048 & 0.5788 \(\pm\) 0.0214 & 0.2252 \(\pm\) 0.0076 & 0.3793 \(\pm\) 0.0162 \\ F1 & FentWalk & 0.8453 \(\pm\) 0.0178 & 0.8789 \(\pm\) 0.0031 & 0.9629 \(\pm\) 0.0047 & 0.6966 \(\pm\) 0.0174 & 0.8604 \(\pm\) 0.0045 & 0.6419 \(\pm\) 0.0236 & **0.3323** \(\pm\) 0.0122 & 0.4944 \(\pm\) 0.0148 \\ \hline Moro-averaged & CAR & 0.8174 \(\pm\) 0.0176 & 0.4652 \(\pm\) 0.0114 & 0.7160 \(\pm\) 0.0126 & 0.7067 \(\pm\) 0.0132 & 0.8051 \(\pm\) 0.0058 & 0.5946 \(\pm\) 0.0192 & 0.2804 \(\pm\) 0.0101 & 0.3900 \(\pm\) 0.0135 \\  & CAN & **0.8077** \(\pm\) 0.0161 & 0.7749 \(\pm\) 0.0095 & 0.8086 \(\pm\) 0.0012 & 0.7599 \(\pm\) 0.0127 & 0.8032 \(\pm\) 0.0057 & **0.6534** \(\pm\) 0.0216 & 0.3299 \(\pm\) 0.0100 & **0.5340** \(\pm\) 0.0155 \\ P1 & DIG & 0.8551 \(\pm\) 0.0128 & 0.4643 \(\pm\) 0.0105 & 0.7396 \(\pm\) 0.0118 & 0.7598 \(\pm\) 0.0112 & **0.8681** \(\pm\) 0.0047 & 0.5899 \(\pm\) 0.0122 & 0.2800 \(\pm\) 0.0064 & 0.3700 \(\pm\) 0.0157 \\  & FastWalk & 0.8589 \(\pm\) 0.0161 & **0.5967** \(\pm\) 0.0057 & **0.9633** \(\pm\) 0.0045 & 0.7402 \(\pm\) 0.0121 & 0.8631 \(\pm\) 0.0054 & 0.6598 \(\pm\) 0.0236 & **0.3546** \(\pm\) 0.0118 & 0.4966 \(\pm\) 0.0154 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results in Task 1, i.e., classification fine tuned via validation set, on attributed networks, using random search to tune hyperparameters. Best results are shown in bold.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cona**} & **Fickick** & **BigCatalog** & **Citeseer** & **Pubmed** & **Chameleon** & **Finn** & **Squired** \\ \hline  & CAR & 0.8839 \(\pm\) 0.0151 & 0.8474 \(\pm\) 0.0185 & 0.7315 \(\pm\) 0.1063 & 0.8532 \(\pm\) 0.0158 & 0.8885 \(\pm\) 0.0049 & 0.9797 \(\pm\) 0.0034 & 0.8045 \(\pm\) 0.0009 & 0.9113 \(\pm\) 0.0133 \\ ROC & CAN & **0.8945** \(\pm\) 0.0079 & 0.8573 \(\pm\) 0.0031 & 0.8588 \(\pm\) 0.0036 & 0.89