## 4 Experimentation
### Experimental Results
#### 4.5.1 Comparison Experiment
BoxTE achieved relatively similar results, with slight decreases of 0.9, 0.4, 1.9, and 2.9 in the four metrics. BoxTE models temporal order constraints and KG structural constraints while effectively modeling uneven time spans using the Box embedding model. It can handle the temporal characteristics of TKG well. However, BoxTE's model tends to be overly smooth, leading to the loss of fine-grained information related to sudden events. This makes it challenging for BoxTE to identify and link such events. RoAN and RotateQVS exhibit similar results but differ significantly from HCAE in performance. This is because RoAN's relation attention mechanism is too simplistic to learn complex temporal patterns thoroughly. At the same time, RotateQVS's quaternion space assumption is overly idealized and unsuitable for capturing complex relationships in real-world TKGs. Additionally, both methods assume fixed time intervals, whereas time gaps often exhibit irregular patterns. HCAE achieves the best results because the GDELT dataset has a relatively small number of entities and relationships but a highly complex topology. Furthermore, some facts persist across multiple consecutive timestamps, while others are instantaneous and sparse. These benchmark methods struggle to extract and model structural information in such scenarios effectively. HCAE, on the other hand, has improved attribute embedding and embedding propagation layers, allowing it to adapt effectively to complex graph structures. It demonstrates strong capabilities in learning complex topology and temporal induction. Table 3 presents the comparison results between HCAE and several TKGC methods on ICEWS14 and ICEWS05-15 datasets. On the ICEWS14 dataset, ChronoR(b) outperforms HCAE by 0.4 in terms of MRR metric, while TeV-SA surpasses HCAE by 0.3 in the Hits@10 metric. On the ICEWS05-15 dataset, TeV-GRU outperforms HCAE by 0.4 in Hits@10 metric. This is because the time information provided by ICEWS14, and ICEWS05-15 is limited. The relationships in these two datasets could be sparser as they involve fewer events such as negotiations, visits, and conflicts. Additionally, different entities are primarily unrelated in time and exhibit limited patterns, resulting in reduced richness and variability of temporal information in the dataset, relying more on entity-based predictions. On the other hand, HCAE models multi-scale historical details, to some extent depending on temporal information, leading to slightly lower performance than the baseline methods in specific cases. However, it is essential to note that HCAE significantly outperforms other baseline methods in most cases. For instance, it exceeds ChronoR(b) by 0.2, 1.8, and 6.4 percentage points in Hits@1/3/10 metrics. Additionally, the performance of TeV-SA and TeV-GRU is notably inferior to HCAE in other metrics. Specifically, HCAE is substantially improved when compared with recent TKGC methods such as RoAN, RotateQVS, and TASTER. Due to the inclusion of many unprecedented new events in event-based KG datasets, HCAE can exploit these new events' historical and non-historical dependencies to uncover latent patterns that have not been observed. Ultimately, it is proven that HCAE, which integrates high-order connectivity and attribute information, is an effective TKGC method.