# Knowledge Graph Alignment Network with Node-Level Strong Fusion
## 5 Experiments
### Experimental Results
#### 5.4.4 Analysis  
An experiment is conducted on the proportional sensitivity of pre-aligned seeds. The proportion of pre-aligned seed pairs is adjusted between 10% and 40% in steps of 10% at a time. MRAEA is the model with superior stability in baseline, as shown in Figure 4. From the previous introduction in this paper, it can be seen that relation embedding is calculated by entity embedding. So, SFEA is compared with HGCN-R, the baseline model with the best performance in the relation alignment comparison experiment, as shown in Figure 5. Hits@1 as a calibration indicator is used on DBP15K. According to the analysis in Figures 4 and 5, the alignment effectiveness of SFEA and comparison models on the DBP15K also gradually improve with the gradual increase in the proportion of pairs of pre-aligned seeds. In this proportion range, SFEA consistently achieves better results and a more gradual increase in performance compared to the comparison model. In other words, SFEA not only has better alignment accuracy and is insensitive to seed scale, but is more stable and robust. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **S-DBP15K\({}_{\text{ZH-EN}}\)** & **S-DBP15K\({}_{\text{JA-EN}}\)** & **S-DBP15K\({}_{\text{FR-EN}}\)** \\  & **Hits@1** & **Hits@10** & **Hits@1** & **Hits@10** & **Hits@1** & **Hits@10** \\ \hline BootEA & 0.122 & 0.275 & 0.278 & 0.526 & 0.327 & 0.532 \\ GCN-Align & 0.114 & 0.264 & 0.213 & 0.451 & 0.292 & 0.489 \\ HGCN & 0.529 & 0.644 & 0.586 & 0.756 & 0.772 & 0.886 \\ RDGCN & 0.607 & 0.746 & 0.693 & 0.829 & 0.836 & 0.926 \\ NMN & 0.620 & 0.751 & 0.703 & 0.844 & 0.863 & 0.94 \\
**SFEA** & **0.665** & **0.787** & **0.751** & **0.868** & **0.883** & **0.955** \\ \hline (w/o SF) & 0.550 & 0.696 & 0.655 & 0.804 & 0.761 & 0.868 \\ (w/o UE) & 0.609 & 0.767 & 0.728 & 0.856 & 0.821 & 0.923 \\ (w/o IS) & 0.630 & 0.780 & 0.741 & 0.866 & 0.846 & 0.945 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance on entity alignment (S-DBP15K). Figure 6 shows that SFEA considers different hop counts for neighborhood information. It can be seen that the performance of SFEA also degrades when it has more layers of GCNs. Although more layers can learn more neighborhood information, those multi-hop neighborhoods may introduce more noise into the model. Therefore, the neighbors of two hops will obtain better results, which indicates that aggregating the neighbor information of two hops is enough. Four aspects of information are respectively modeled, including attribute information (attr), relationship information (rel), structure information (struc) and entity name information (name). As shown in Figure 7, the entity alignment performance of these four aspects, SFEA (w)o SF), and SFEA model are studied. It can be concluded as follows:  
Figure 4: Different proportions of pre-aligned seed (entity alignment). Figure 5: Different proportions of pre-aligned seed (relation alignment). Figure 6: Different layers of GCN (entity alignment). 1. In the comparison of four different aspects of information, it can be observed that entity names perform better than other alone aspects of information. The reason is that the English and non-English language versions in the DBP15K dataset are all from Wikipedia, with a large number of entity pairs for which the non-English entities can be converted into names that are highly similar or even the same as the corresponding English entities after machine translation. However, in practical situations, the alignment task needs to deal with the same objects with different names, and different objects with the same names. So, entity alignment cannot just align by entity name without considering other information, or placing entity names too high. 2. The effect of the (w\(\backslash\)o SF) model is not obviously compared to using only structure information, which shows that this simple aspect-level connection operation does not efficiently integrate information from different aspects, and even existing alignment signals would be lost.