# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 5 Experiments
### Overall Results on DBP-15K
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{3}{c}{**ZH-EN**} & \multicolumn{3}{c}{**JA-EN**} & \multicolumn{3}{c}{**FR-EN**} \\
**Models** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** \\ \hline \hline \multicolumn{10}{c}{Supervised methods with pure relation structures} \\ \hline MTransE [56] & 30.8 & 61.4 & 36.4 & 27.9 & 57.5 & 34.9 & 24.4 & 55.6 & 33.5 \\ IPTransE [57] & 40.6 & 73.5 & 51.6 & 36.7 & 69.3 & 47.4 & 33.3 & 68.5 & 45.1 \\ BootEA [54] & 62.9 & 84.8 & 70.3 & 62.2 & 85.4 & 70.1 & 65.3 & 87.4 & 73.1 \\ RDGCN [58] & 70.8 & 84.6 & 74.9 & 76.7 & 89.5 & 81.2 & 88.6 & 95.7 & 90.8 \\ AliNet [59] & 53.9 & 82.6 & 62.8 & 54.9 & 83.1 & 64.5 & 55.2 & 85.2 & 65.7 \\ EMEA * [60] & 78.2 & 93.3 & 84.2 & 77.1 & 95.0 & 83.7 & 80.1 & 96.6 & 86.3 \\ RPR-RHGT [7] & 69.3 & 86.9 & 75.4 & 88.6 & 95.5 & 91.2 & 88.9 & 97.0 & 91.0 \\ STEA * [61] & 84.1 & 88.4 & 85.9 & 84.3 & 91.2 & 87.2 & 88.9 & 96.9 & 91.8 \\ PEEA *† [9] & 76.1 & 91.5 & 81.6 & 77.2 & 92.5 & 82.1 & 80.6 & 94.5 & 85.8 \\ RANM [8] & 77.6 & 88.1 & 81.3 & 90.5 & 95.2 & 92.3 & 90.9 & 95.8 & 92.7 \\ KAGNN [62] & 73.6 & 87.3 & 78.6 & 79.4 & 91.1 & 83.7 & 92.0 & 97.6 & 94.1 \\
**Gain** & **+6.38** & \(-\)0.2 & +3.18 & \(-\)0.98 & +0.32 & \(-\)0.53 & **+6.85** & +2.33 & +5.23 \\ \hline \hline \multicolumn{10}{c}{Supervised methods with auxiliary information} \\ \hline JAPE [53] & 41.2 & 74.5 & 49.0 & 36.3 & 68.5 & 47.6 & 32.4 & 66.7 & 43.0 \\ GCN-Align [63] & 41.3 & 74.4 & 54.9 & 39.9 & 74.5 & 54.6 & 37.3 & 74.5 & 53.2 \\ MRAEA [24] & 75.7 & 92.9 & 82.7 & 75.7 & 93.3 & 82.6 & 78.0 & 94.8 & 84.9 \\ AttrGNN † [27] & 79.6 & 92.9 & 84.5 & 78.3 & 92.0 & 83.4 & 91.8 & 97.7 & 91.0 \\ MHNA * [29] & 60.3 & 80.5 & 65.7 & 87.6 & 94.4 & 90.3 & 87.8 & 95.0 & 90.5 \\ MMA-cat ‡ [64] & 62.4 & 84.5 & 70.2 & 64.1 & 86.9 & 72.3 & 72.5 & 91.4 & 79.3 \\ GEEA ‡ [39] & 76.1 & 94.6 & 82.7 & 75.5 & 95.3 & 82.7 & 77.6 & 96.2 & 84.4 \\
**Gain** & **+4.38** & \(-\)1.5 & +2.88 & **+1.92** & +0.52 & +1.47 & **+7.05** & +2.03 & +8.33 \\ \hline \hline \multicolumn{10}{c}{Unsupervised methods} \\ \hline MultiKE [10] & 43.7 & 51.6 & 46.6 & 57.0 & 64.2 & 59.6 & 71.4 & 76.0 & 73.3 \\ AttrE [65] & 26.3 & 43.6 & 32.2 & 38.1 & 61.5 & 47.5 & 62.3 & 79.3 & 68.6 \\ EVA ‡ [11] & 75.2 & 89.5 & 80.4 & 73.7 & 89.0 & 79.1 & 73.1 & 90.9 & 79.2 \\ SEU *† [43] & 80.8 & 92.1 & 85.2 & 87.1 & 94.6 & 89.8 & 97.0 & 99.6 & 98.3 \\ SelfKG † [14] & 73.8 & 86.0 & 77.1 & 81.5 & 91.3 & 84.9 & 94.2 & 98.8 & 97.2 \\ ICLEA † [13] & 80.4 & 91.4 & - & 87.3 & 93.1 & - & 97.3 & 99.5 & - \\ UPLR † [12] & 82.0 & 91.6 & 85.6 & 88.0 & 94.9 & 90.8 & 97.8 & 99.5 & 98.5 \\ UDCEA † [44] & 81.1 & 92.2 & 85.5 & 84.7 & 93.5 & 87.8 & 98.1 & 99.5 & 98.7 \\ _MPGT-Align †_ & **83.98** & **93.10** & **87.38** & **89.52** & **95.82** & **91.77** & **98.85** & **99.93** & **99.33** \\
**Gain** & **+1.98** & +0.90 & +1.78 & **+1.52** & +0.92 & +0.97 & **+0.75** & +0.33 & +0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative results of _MPGT-Align_ against 26 baselines on _DBP-15K_. Results with underlining represent the best results among each category of baselines, while **bold** formatting highlights the top performance among unsupervised methods. methods for achieving alignment learning. Furthermore, _MPGT-Align_ employs only Transformer networks, with multi-hop feature acquisition conducted prior to model training. In contrast, other unsupervised methods exhibit greater complexity in their model architectures. For instance, the Sinkhorn operation utilized by SEU [43] incurs an algorithmic complexity of \(O(n^{2})\), while the self-negative sampling strategy employed by SelfKG necessitates maintaining two negative sample queues. Thus, _MPGT-Align_ possesses certain advantages over its counterparts among unsupervised methods. In summary, _MPGT-Align_ surpasses the performance ceiling of current state-of-the-art EA methods and validates the effectiveness of our design.