## 2 Related Works
### Knowledge-Graph-Based DHIN Embedding  
In recent years, Knowledge Graphs have attracted a great deal of attention, and Knowledge Graphs are essentially heterogeneous networks that contain entities and relationships in networks [32]. According to modeling methods, they can be divided into Translation models, Bilinear models, Rotation models, Temporal Information Point Process models, Probability Distribution models, and Graph Neural Network models, etc.  
The Translation model TransE [33] embeds the vector of the tail entity as the vector of the head entity plus the vector of the relationship, which can learn the rich semantics of entities and relationships. The Bilinear model RESCAL [34] models the relationships as a linear mapping matrix from the head entity to the tail entity. The Rotate model [35] solves the defect that the Translation model and the Bilinear model can only model part of the relationships and can model and infer all types of relationships in Knowledge Graphs. The Temporal Information Point Process model introduces temporal information and captures temporal information and dynamic properties in Knowledge Graphs. For example, the Know-Evolve temporal point model [36] can be used to model and predict future events, but it cannot model concurrent events that occur in the same time window. The Probability Distribution model RE-NET [37] solves the shortcomings of the Temporal Information Point Process methods and models events of the historical time series as a conditional probability distribution, but RE-NET cannot solve the long-term dependency problem, and the embedding of all time steps contributes the same to the current embedding. The GNN models, represented by RE-GCN [38], can capture the dependencies between entities under the same timestamp through a message-passing framework and capture sequential information patterns through the stacking of neural graph networks. However, the RE-GCN model may suffer from over-smoothing and overfitting problems.  
At present, Knowledge Graph embedding and traditional HIN embedding are gradually integrated. For example, MPDRL [39] learns semantics through reinforcement learning on the basis of Knowledge Graphs; RHINE [40] draws on Knowledge Graph learning technology, divides relationships in heterogeneous networks into membership and interaction relationships, and learns them separately; [PERSON] et al. [41] combine with neural networks to produce high-throughput, fast and real-time data, which can work for data centers, databases and other platforms as a service. [PERSON] et al. [42] use the Adapter pre-trained by 'linking of GraphQL schemas and corresponding utterances' as an external knowledge introduction plug-in to better map between the logical language and natural language. [PERSON] et al. [43] propose a real quadratic form-based graph pooling framework for GNN, which has stronger expressive power than the existing linear forms in Knowledge Graph. Although real-time Knowledge Graphs have achieved great success through inference means, how to further exploit the complex structure of the network itself is still an open topic [44].  
To sum up, the possible routes for existing dynamic heterogeneous network embedding include HIN embedding, which can be repeatedly called at different time points, dynamic homogeneous network embedding, and dynamic Knowledge Graph embedding. In the experimental part, we will select representative algorithms from the above routes as experimental baselines.