# Neural Graph Similarity Computation with Contrastive Learning
## 4 Experiments and Results
### Experimental Results
#### 4.2.3 Efficiency Comparison  
We further compare the efficiency of these approaches. It can be seen from Table 4 that our proposed framework, Conga, requires the least running time, which is over 30\(\times\) faster than the classic methods and 3\(\times\) faster than the neural methods. This is due to its simple  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{3}{c}{AIDS} & \multicolumn{3}{c}{LINUX} & \multicolumn{3}{c}{IMDB} \\ \hline
**Metrics** & **mse(\(\downarrow\))** & \(\rho(\uparrow)\) & **p@10(\(\uparrow\))** & **mse(\(\downarrow\))** & \(\rho(\uparrow)\) & **p@10(\(\uparrow\))** & **mse(\(\downarrow\))** & \(\rho(\uparrow)\) & **p@10(\(\uparrow\))** \\ \hline A\({}^{*}\) & **0.000** & **1.000** & **1.000** & **0.000** & **1.000** & **1.000** & / & / & / \\ Beam & 12.090 & 0.609 & 0.481 & 9.268 & 0.827 & 0.973 & 2.413 & 0.905 & 0.813 \\ Hungarian & 25.296 & 0.510 & 0.360 & 29.805 & 0.638 & 0.913 & 1.845 & 0.932 & 0.825 \\ VJ & 29.157 & 0.517 & 0.310 & 63.863 & 0.581 & 0.287 & 1.831 & 0.934 & 0.815 \\ HED & 28.925 & 0.621 & 0.386 & 19.553 & 0.897 & 0.982 & 19.400 & 0.751 & 0.801 \\ \hline SimGNN & 1.376 & 0.824 & 0.400 & 2.479 & 0.912 & 0.635 & 1.264 & 0.878 & 0.759 \\ GMN & 4.610 & 0.672 & 0.200 & 2.571 & 0.906 & 0.888 & 4.422 & 0.725 & 0.604 \\ GraphSim & 1.919 & 0.849 & 0.446 & 0.471 & 0.976 & 0.956 & 0.743 & 0.926 & 0.828 \\ GOTSim & 1.180 & 0.860 & **0.870** & 2.125 & 0.920 & 0.860 & 2.960 & 0.850 & 0.730 \\ SGNN & 2.822 & 0.765 & 0.289 & 11.830 & 0.566 & 0.226 & 1.430 & 0.870 & 0.748 \\ NGMN & 1.191 & 0.904 & 0.465 & 1.561 & 0.945 & 0.743 & 1.331 & 0.889 & 0.805 \\ MGMN & 1.169 & 0.905 & 0.456 & 0.439 & 0.985 & 0.955 & 0.335 & 0.919 & 0.837 \\ H\({}^{2}\)MNNW & 0.957 & 0.877 & 0.517 & 0.227 & 0.984 & 0.953 & 0.308 & 0.912 & 0.861 \\ H\({}^{2}\)MNNE & 0.913 & 0.881 & 0.521 & **0.105** & 0.990 & 0.975 & 0.589 & 0.913 & **0.889** \\ \hline Noah & 1.545 & 0.734 & 0.809 & / & / & / & 27.818 & 0.810 & 0.354 \\ GENN-A\({}^{*}\) & 0.839 & **0.953** & 0.866 & 0.324 & 0.991 & 0.962 & / & / & / \\ \hline Conga & **0.823** & 0.892 & 0.550 & 0.121 & **0.992** & 0.987 & **0.288** & **0.948** & 0.871 \\ w/o CL & 0.840 & 0.890 & 0.544 & 0.126 & **0.992** & **0.994** & 0.315 & 0.947 & 0.866 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main experimental results (mse is in the scale of the \(10^{-3}\)). \(\downarrow\) means lower values are preferred, while \(\uparrow\) means higher values are preferred. / represents that the results cannot be obtained due to excessive running time or are not provided in the original work (and cannot be reproduced).  
architecture that merely comprises convolutional layers and pooling functions, where the resource-consuming fine-grained interactions between graphs are abandoned. In addition, it shows that the contrastive learning module brings extra running time.  
From the table, we can also observe that, on average, the neural methods (on the left) are faster than the traditional ones or the ones that build upon traditional frameworks (on the right).