# Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
## 4 Experiment
### Performance Analysis  
Accuracy. The experimental results of network alignment on real-world datasets and synthetic dataset are presented in Table 2, we can draw the following observations. * Based on the experimental results, we find that our method DCSNA can perform better than other baseline methods in most cases, especially under the metric Macro Precision. These results demonstrate DCSNA's superiority, which learns each node representation as a distribution and masks the nodes with a larger degree. Representing nodes as distributions let us distinguish the nodes from their neighbor nodes and reduce confusion during network alignment. This is also why embedding-based methods, such as PALE and CrossMNA, perform slightly worse, as they overly preserve the neighbor structure of nodes, making nodes and their neighbors indistinguishable. Since CAMU is to reduce the node representation distribution between two networks, it performs better than methods that only learn node representations. * Although BRIGHT and NeXtAlign consciously distinguish anchor node pairs from their neighbors through mechanisms, such as sampling or using anchor links as landmarks, the key idea is still keeping the consistency of anchor node pairs. Therefore, they are more suitable for the situation where most anchor nodes satisfy equivalence connection status across networks. * It is worth noting that the performance of DHNA is second only to our model in most cases. DHNA considers the degree discrepancy across nodes, i.e., non-equivalence connection status, and makes a balance between consistency and such connection status. However, it ignores another connection status. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metric** & **Dataset** & **PALE** & **CrossMNA** & **CAMU** & **BRIGHT** & **NeXtAlign** & **DHNA** & **DCSNA** \\ \hline \multirow{4}{*}{Macro Precision} & Dataset.1 & 0.617 & 0.630 & 0.663 & 0.602 & 0.710 & 0.719 & 0.730 \\  & Dataset.2 & 0.589 & 0.629 & 0.654 & 0.617 & 0.649 & 0.672 & 0.727 \\  & Dataset.3 & 0.596 & 0.616 & 0.635 & 0.616 & 0.651 & 0.750 & 0.883 \\  & Dataset.4 & 0.595 & 0.650 & 0.553 & 0.598 & 0.621 & 0.703 & 0.796 \\ \hline \multirow{4}{*}{Macro Recall} & Dataset.1 & 0.554 & 0.620 & 0.646 & 0.626 & 0.697 & 0.662 & 0.676 \\  & Dataset.2 & 0.586 & 0.618 & 0.686 & 0.554 & 0.693 & 0.677 & 0.695 \\  & Dataset.3 & 0.585 & 0.616 & 0.629 & 0.614 & 0.696 & 0.706 & 0.516 \\  & Dataset.4 & 0.594 & 0.630 & 0.549 & 0.622 & 0.629 & 0.688 & 0.796 \\ \hline \multirow{4}{*}{Macro F1} & Dataset.1 & 0.583 & 0.618 & 0.655 & 0.619 & 0.703 & 0.675 & 0.688 \\  & Dataset.2 & 0.585 & 0.619 & 0.670 & 0.584 & 0.670 & 0.673 & 0.704 \\ \cline{1-1}  & Dataset.3 & 0.584 & 0.616 & 0.670 & 0.616 & 0.674 & 0.721 & 0.533 \\ \cline{1-1}  & Dataset.4 & 0.594 & 0.640 & 0.551 & 0.610 & 0.625 & 0.695 & 0.796 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of DCSNA and other methods. Overall, our results highlight the effectiveness of DCSNA in addressing the diverse connection status of anchor nodes and demonstrate its superiority over other state-of-the-art network alignment methods. In addition, we conduct experiments on the synthetic data, to analyze the impact of edge insertions/deletions of networks on network alignment. The result is presented in Figure 4, we compare the three metrics on combinations of different overlap levels \(\alpha_{c}\) and sparsity level \(\alpha_{s}\). For example, \"3_5\" on the horizontal axis means the combination of \(\alpha_{c}=0.3\) and \(\alpha_{s}=0.5\). It is obvious that performance becomes better when \(\alpha_{c}\) and \(\alpha_{s}\) increase. This is because as \(\alpha_{c}\) and \(\alpha_{s}\) increase, the network becomes denser and the two networks share more edges, which is beneficial for network alignment. It is worth noting that the results of DCSNA on various combinations are not very different, which means that our model is not sensitive to the insertions/deletions of edges in the networks. Efficiency.