## 2 Related Works
### Temporal Knowledge Graph Completion  
Many TKGC models extend existing SKGC models by introducing time embeddings or modeling the temporal dimension, to capture the evolution of entities and relationships at different time points. For example, TTransE [31] is an extension of the TransE model that encodes timestamps as translation operations to perform additional translation on the representations of head entities. TBDRI [32] is a block term decomposition (similar to a variant of Tucker decomposition) model based on binary tensor representation of KG quadruples. It enhances the binding of forward and backward relations and introduces a core tensor as temporal information to capture temporal relationships between entities better. ChronoR [33] uses the RotatE model as a foundation and combines the timestamp with the relation representation to form a unified rotation vector. This enables the representation of temporal relationship pairs with rotation and scaling in the embedding space. TComplEx [34] and TNTComplEx [34] are extensions of ComplEx. The former applies factorization methods to model a fourth-order tensor, while the latter decomposes the tensor into temporal and non-temporal components during the factorization process. TASTER [35] represents different TKGs using sparse transformation matrices to reduce the complexity of entity relation evolution.  
Fusion neural networks are another vital research direction for TKGC models. TA-DistMult [36] learns time-aware relationship representations using recursive neural networks (RNN) based on DistMult and TransE models, injecting the capability of temporal modeling into these two SKGC models. TeMP [37] utilizes message-passing graph neural networks (GNN) to learn structure-based entity representations at each timestamp. It aggregates the representations of all timestamps using TeMP-GRU and TeMP-SA encoders, capturing both time dependency and structure dependency. FTMF [38] encodes entities using self-attention mechanisms, aggregates parameter sets using RNNs, and employs a fault-tolerant mechanism to handle incorrect information. It effectively handles few-shot TKGC tasks. SANe [39] maps facts corresponding to different timestamps to different latent spaces using a convolution neural network (CNN). It explores the overlap in the latent space through a time-aware parameter generator to capture contextual information about timestamps. Many existing studies rely on handcrafted GNNs, which cannot fully explore the diverse topology and temporal information of TKGs. To address this, SPA [40] adopts neural architecture search (NAS) techniques to design a general framework. Through single-path sampling training, it efficiently searches for the optimal message-passing structure on various datasets. RoAN [41] is a time-oriented attention network that embeds time features by applying attention mechanisms to multi-chain relationships in TKGs. ExKGR [42] employs GNNs to achieve interpretable multi-hop completion. TARGCN [43] is a simple but powerful graphical encoder with valid parameters. The contextual information of each entity can be extensively mined.  
The two types of methods described above require predefined temporal granularity and modeling based on this granularity. Nevertheless, temporal information in TKG exhibits multi-granularity, and the time scales for the evolution of different facts or relationships are other. Using a fixed single time scale makes capturing historical information at additional time granularities challenging and will lead to inadequate modeling of specific temporal patterns, thus reducing the expressive power of the model. Therefore, designing a model that can support multi-timescale modeling in the TKGC task is a direction worth exploring in depth. Based on multi-timescale modeling, HCAE enhances the model's ability to complement TKG by learning attribute information and high-order connectivity.