# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 5 Results
### Ablation Study  
The encoder of the MSEN model is mainly composed of two submodules: Local Memory Encoder (LME) and Global Memory Encoder (GME). To further analyze the effect of each module on the final entity prediction results, the paper reports the results of LME (HT-GNN) and GME (TR-GNN) on the ICEWS14 dataset using MRR and Hit@k (k = 1, 3, 10) metrics. LME (HT-GNN) represents removing the GME module from the MSEN model; GME (TR-GNN) represents removing the LME module from the MSEN model. As can be seen from Table 5, the performance of LME (HT-GNN) is worse than MSEN, because this part only extracts local features and the obtained embedding representations have less semantic information. The performance of GME (TR-GNN) has greatly improved compared to LME (HT-GNN), but integrating both using a gating function can further improve the accuracy of entity prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MRR** & **Hit@1** & **Hit@10** \\ \hline DistMult & 16.59 & 10.01 & 31.69 \\ ComplEx & 18.84 & 11.41 & 25.78 \\ RGCN & 18.04 & 8.57 & 35.68 \\ ConvE & 24.28 & 15.61 & 44.59 \\ RotatE & 15.35 & 7.10 & 33.09 \\ CyGNet & 27.12 & 17.21 & 46.85 \\ RE-NET & 29.78 & 19.73 & 48.46 \\ xERTE & 29.31 & 21.03 & 46.48 \\ TTfer & 29.98 & 22.05 & 44.83 \\ RE-GCN\({}^{\star}\) & 30.13 & 19.11 & 48.86 \\ CENET & 29.65 & 19.98 & 48.23 \\ TiRGN\({}^{\star}\) & 32.22 & 22.24 & 51.88 \\
**Our Method** & **34.05** & **22.83** & **56.80** \\ \hline \hline \end{tabular}  
* The mark \({}^{\star}\) in the table represents removing static information from models for fair comparison [15]. The best results are in bold.  
\end{table}
Table 4: Performance on the ICEWS18 dataset in terms of MRR (%), Hit@1 (%), and Hit@10 (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**ICEWS14**} \\ \cline{2-5}  & **MRR** & **Hit@1** & **Hit@3** & **Hit@10** \\ \hline LME (RGCN) & 38.69 & 29.34 & 43.11 & 56.68 \\ LME (KBGAT) & 38.74 & 29.50 & 43.25 & 56.26 \\ LME & 39.08 & 29.78 & 43.75 & 56.77 \\ LME (HT-GNN) & 39.91 & 30.45 & 44.48 & 57.91 \\ \hline GME (RGCN) & 43.84 & 31.29 & 49.90 & 69.06 \\ GME (KBGAT) & 43.41 & 31.53 & 48.83 & 67.08 \\ GME & 43.72 & 31.11 & 49.86 & 68.98 \\ GME (HRCNN) & 44.93 & 33.02 & 50.58 & 68.97 \\ GME (TR-GNN) & 45.24 & 33.36 & 50.88 & 69.22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of GNN-based model in LME and GME in terms of MRR (%), Hit@1 (%), Hit@3 (%), and Hit@10 (%).  
To study the effects of HT-GNN and TR-GNN in LME and GME, respectively, the paper compares these two models with different types of GNN-based models on the ICEWS14 dataset using MRR and Hit@k (k = 1, 3, 10) metrics. In LME, RGCN [3], KBGAT [31], and CompGCN (add) [32] are used to replace the HT-GNN model; in GME, RGCN [3], KBGAT [31], CompGCN (add) [32], and HRGNN [28] are used to replace the TR-GNN model. The experimental results in Table 5 show that the MRR of HT-GNN is 0.83 percentage points higher than CompGCN (add); in GME, the MRR of TR-GNN is 0.31 percentage points higher than HRGNN. Overall, both can effectively improve the accuracy of entity prediction in LME and GME respectively. The improvement of HT-GNN may be due to the hierarchical transfer method that enriches the semantic information of entities in the final knowledge graph. The improvement of TR-GNN may be due to its full utilization of both periodic and non-periodic time in the global knowledge graph.