## 4 Experimentation
### Baselines  
To verify the validity of the HCAE model, comparative tests with the four SKGC methods and the existing state-of-the-art TKGC methods will be conducted in this paper. The introduction of the benchmark methods is described below:  
1. TransE [18] learns low-dimensional embeddings by translating entity and relationship vectors, achieving KGC tasks.
2. DistMult [23] learns entity and relationship representations by using the inner product of entity and relationship triplets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Entities** & **Relations** & **Training** & **Validation** & **Test** & **Interval** \\ \hline GDELT & 7691 & 240 & 1,734,399 & 238,765 & 305,241 & 15 min \\ ICEWS14 & 6869 & 230 & 74,845 & 8514 & 7371 & 24 h \\ ICEWS05-15 & 10,094 & 251 & 368,868 & 46,302 & 46,159 & 24 h \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of datasets.  
CompIEx [24] represents entities and relationships as complex-valued embeddings and utilizes complex-valued inner products to capture symmetric/antisymmetric relationships, thereby improving KG link prediction performance.
4. R-GCN [26] effectively captures complex relationships and contextual information between nodes.
5. TTransE [31] innovatively embeds spatiotemporal information into KG, which provides richer contextual information for entity and relationship representation.
6. TA-DistMult [36] is the first approach to introduce a time-sensitive attention mechanism in the TKGC task, which can enhance the presentation of relations across time.
7. ChronoR [33], in which the representations of the relationship and timestamp are concatenated and applied as a rotation vector to the entity, enabling the temporal representation of the relationship to possess time dependency.
8. TNTCompIEx [34] decomposes the KG fourth-order tensor into material and non-temporal components and then specializes in learning the earthly manifestation of relations to achieve dynamic completion of KG.
9. TeMP [37], in which, using message passing GNN, the structured representation of each entity at each time step is learned. The dynamic entity representation is obtained by aggregating all of the representations through an encoder.
10. MtGCN [13] uses GCN to mine latent semantic information in TKG. GRU explicitly models historical information at different scales and uses the generated valid entity and relationship representations to complete TKGC.
11. BoxTE [47] introduces dedicated time embedding representations, enabling the relationship representation at each time point to be unique and highly expressive.
12. RoAN [41] directly encodes relationships and captures their temporal characteristics through a multi-chain structure and relationship attention mechanism.
13. TASTER [35] learns global and local TKG embeddings using a sparse transition matrix, considering global statistics and local temporal evolution information.
14. RotateQVS [48] represents entities and relationships in a quaternion vector space, utilizing rotation to represent the temporal evolution of relationships while also considering patterns such as symmetry and antisymmetry in relationships.