Recurrent Event Network: Global Structure Inference over Temporal Knowledge Graph
Table 4: Performance comparison on ICEWS and GDELT datasets with raw metrics. We observe our method outperforms all other methods.
['[BOLD] Method', '[BOLD] Method', '[BOLD] ICEWS18 - raw MRR', '[BOLD] ICEWS18 - raw H@1', '[BOLD] ICEWS18 - raw H@3', '[BOLD] ICEWS18 - raw H@10', '[BOLD] GDELT - raw MRR', '[BOLD] GDELT - raw H@1', '[BOLD] GDELT - raw H@3', '[BOLD] GDELT - raw H@10', '[BOLD] ICEWS14 - raw MRR', '[BOLD] ICEWS14 - raw H@1', '[BOLD] ICEWS14 - raw H@3', '[BOLD] ICEWS14 - raw H@10']
Our proposed RE-Net outperforms all other baselines. Figs. Our proposed RE-Net consistently outperform baselines over time.