## 3 Results
### Result Analysis
If these complex structural dependencies are ignored, there will be a lot of loss in modeling sequential patterns. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**ICEWS14**} & \multicolumn{6}{c}{**ICEWS15**} & \multicolumn{6}{c}{**ICEWS18**} \\ \cline{2-13}  & **MRR** & **hit1** & **hit3** & **hit10** & **MRR** & **hit1** & **hit3** & **hit10** & **MRR** & **hit1** & **hit3** & **hit10** \\ \hline Distmult & 20.32 & 6.13 & 27.59 & 46.61 & 19.91 & 5.63 & 27.22 & 47.33 & 13.86 & 5.61 & 15.22 & 31.26 \\ ConvE & 30.30 & 21.30 & 34.42 & 47.89 & 31.40 & 21.56 & 35.70 & 50.96 & 22.81 & 13.63 & 25.83 & 41.43 \\ RGCN & 28.03 & 19.42 & 31.95 & 44.83 & 27.13 & 18.83 & 30.41 & 43.16 & 15.05 & 8.13 & 16.49 & 29.01 \\ HyTE & 16.78 & 2.13 & 24.84 & 43.94 & 16.05 & 6.53 & 20.2 & 34.72 & 7.41 & 3.11 & 7.33 & 16.01 \\ \hline TTransE & 12.86 & 3.14 & 15.72 & 33.65 & 16.53 & 5.51 & 20.77 & 39.26 & 8.44 & 1.85 & 8.95 & 22.38 \\ TA-Distmult & 26.22 & 16.83 & 29.72 & 45.23 & 27.51 & 17.57 & 31.46 & 47.32 & 16.42 & 8.61 & 18.13 & 32.51 \\ R-GCRN & 33.31 & 24.08 & 36.55 & 51.54 & 35.93 & 26.23 & 40.02 & 54.63 & 23.46 & 14.24 & 26.62 & 41.96 \\ RENET & 35.77 & 25.99 & 40.10 & 54.87 & 36.86 & 26.24 & 41.85 & 57.60 & 26.17 & 16.43 & 29.89 & 44.37 \\ REGCN & 37.78 & 27.17 & 42.50 & **58.84** & 38.27 & 27.43 & 43.06 & 59.93 & 27.51 & 17.82 & 31.17 & 46.55 \\ \hline
**ERGCN (ours)** & **37.95** & **28.77** & **42.54** & 55.32 & **43.65** & **33.48** & **48.92** & **62.94** & **28.29** & **18.46** & **32.60** & **47.47** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of entity prediction on ICEWS14, 15 and 18 in raw metrics. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**WIKI**} & \multicolumn{6}{c}{**YAGO**} & \multicolumn{6}{c}{**GDELT**} \\ \cline{2-13}  & **MRR** & **hit1** & **hit3** & **hit10** & **MRR** & **hit1** & **hit3** & **hit10** & **MRR** & **hit1** & **hit3** & **hit10** \\ \hline Distmult & 27.96 & - & 32.45 & 39.51 & 44.05 & - & 49.70 & 59.94 & 8.61 & 3.91 & 8.27 & 17.04 \\ ConvE & 26.03 & - & 30.51 & 39.18 & 41.22 & - & 47.03 & 59.90 & 18.37 & 11.29 & 19.36 & 32.13 \\ RGCN & 13.96 & - & 15.75 & 22.05 & 20.25 & - & 24.01 & 37.30 & 12.17 & 7.40 & 12.37 & 20.63 \\ HyTE & 25.40 & - & 29.16 & 37.54 & 14.42 & - & 39.73 & 46.98 & 6.69 & 0.01 & 7.57 & 19.06 \\ \hline TTransE & 20.66 & - & 23.88 & 33.04 & 26.10 & - & 36.28 & 47.73 & 5.53 & 0.46 & 4.97 & 15.37 \\ TA-Distmult & 26.44 & - & 31.36 & 38.97 & 44.98 & - & 50.64 & 61.11 & 10.34 & 4.44 & 10.44 & 21.63 \\ R-GCRN & 28.68 & - & 31.44 & 38.58 & 38.58 & - & 43.71 & 48.53 & 18.63 & 11.53 & 19.81 & 32.42 \\ RENET & 30.87 & - & 335.55 & 41.27 & 46.81 & - & 52.71 & 61.93 & **19.60** & **12.03** & **20.56** & **33.89** \\ REGCN & 39.84 & **-** & 44.43 & 53.88 & **58.27** & **-** & **65.62** & **72.97** & 19.15 & 11.92 & 20.41 & 33.19 \\ \hline
**ERGCN (ours)** & **51.28** & **-** & **55.78** & **62.28** & 55.25 & - & 64.22 & 70.69 & 18.96 & 11.65 & 20.24 & 33.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of entity prediction on WIKI, YAGO and GDELT in raw metrics. The results demonstrate that ERGCN is more capable of learning the complex temporal structures in TKG. ERGCN's performances on YAGO and GDELT are a little worse than the previous best. YAGO consists of lots of temporal facts with repetitive patterns. ERGCN does not handle this problem well, but this phenomenon does not appear in other datasets. GDELT includes massive concepts and definitions that follow specific rules. This situation makes entity reasoning difficult. The results of all models are similarly poor in GDELT. It is noticed that the results of all methods on ICEWS18 and GDELT are still at a low level. For example, the \(MRR\) is under 20% and 30% in GDELT and ICEWS18, respectively.