will win the next US presidential election. We prepare a example of entity prediction in Figure 1. Extrapolation research is not only of practical significance, but also theoretical value, because studying the evolution of facts can help us understand the informative relationship hidden behind the structural knowledge graphs. There have been many efforts focused on this problem but it is far from being solved. The entity prediction tasks on knowledge graphs can be separated into two parts--static prediction methods and dynamic prediction models. According to the optimization targets, static prediction methods can be further classified into three types: distance-based methods, semantic similarity-based methods and deep learning methods. Among the distance-based methods, TransE [4] is a classical approach to interpret relations on KGs. TransE regards the representation of entities and relations as transitional vectors, the goal is to minimize the distance of representation in the triple \((s,r,o)\), i.e., \(min||s+r-o||\). Based on the idea, TransD [13], TransR [14] and TransH [14] were proposed with different weight matrices to transfer entities' vectors before scoring the distance loss. The semantic similarity-based methods, e.g., DistMult [5], use a bi-linear function to calculate the plausibility in the triple. Some studies on knowledge graph completion follow this idea, such as HolE [15] and Ripplenet [3]. The scoring function is generally formed as \(f(s,r,o)=s^{T}W_{r}o\) where \(W_{r}\) is a parameter matrix to represent the relation types. A popular genre of deep learning methods for entity prediction in KGs is GCN-based approaches, such as GAT [16], SAGE [17]. A GCN-based block consists of multiple layers of neural network blocks to generate hidden representations of entities which include rich semantic information of context. A common GCN block is formed as \(h_{s}^{(l+1)}=\sigma(\sum_{m\in M_{s}}g_{m}(h_{s}^{l},h_{j}^{l}))\) Here, \(h_{s}^{l},h_{j}^{l}\) represent the hidden representations of entity \(s\) and its related entity \(j\) in the \(l\)-th layer. \(M_{s}\) denotes the set of neighbors of entity \(s\). \(g_{m}\) is a specific neural network function for propagating messages. For instance, [PERSON] et al. [18] propose a linear parameter matrix to transform representations of entities and their neighbors. GAT [16] uses a local attention weight to distinguish the importance of the target entity's neighbors. SAGE [17] concatenates the embedding of a target entity and its neighbors as a type of feature vector which is able to reserve more original features of the target. Different from models mentioned above for graphs where there is only one type of relations, RGCN [19] is a notable approach which introduces a relational specific transformation function to deal with multiple relation types. As static methods ignore the influence of time, they cannot model the evolutionary trend of facts on knowledge graphs. In order to predict future facts based on histories, dynamic prediction models try to train time-varying representation of facts to reflect the evolution over time. Several works modified static methods to adapt to the temporal change of data. One aspect of efforts adds extra weights or features to entities' representations, such as Time-Aware [12], TA-TransE [11], DE-TransE [20]. The comparative experiments show that dynamic methods which learn the evolution of facts perform better. Time-aware [12] is an early work to predict the changes of relations on TKGs. It uses an asymmetric matrix to translate the relation matrix of TransE and add integer programming as constraints to capture temporal features. TTransE [21] uses a series of weights to represent the relations on different time. TA-TransE [11] directly defines the representation of time as a series of vectors. DE-TransE [20] creates a diachronic method to represent evolution of entities. Know-Evolve [10] and its follow-up Dyrep [22] use RNN-based models to create dynamic representation of entities.