# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 3 Deep Learning-Based TKGC Methods
### GCN-Based Methods  
Graph convolutional network (GCN) [64; 65] is a variant of GNN that uses a convolution-like operation to aggregate the features of each node's corresponding neighbor nodes and then performs a non-linear transformation to update the feature vector of each node. Formally, at the \(k\)-th layer, the feature vector of each node is computed as (the aggregate and update function are integrated):  
\[h_{v}^{(k)}=\sigma(\sum_{u\in N(v)\cup\{v\}}Wh_{u}^{(k-1)}), \tag{7}\]  
where \(W\) is a weight matrix, \(\sigma\) is the activate function ReLU, and \(N(v)\) is the set of neighbor nodes of \(v\). TeMP [40] is a GCN-based method that has an encoder-decoder structure. The encoder maps each entity to a time-dependent low-dimensional embedding at each time step, while the decoder uses these entity embeddings to score the likelihood of the facts. Specifically, it discretizes a TKG into multiple static KG snapshots and generates dynamic entity features along two dimensions: structural neighborhoods and temporal dynamics. Structural encoder uses GNN to extract features from local node neighborhoods in each snapshot through message passing and aggregation, while temporal encoder captures feature evolution over multiple time steps by sequential models. To the decoder, the score function of TransE [17] is applied to calculate the plausibility of the facts. MiGCN [41] also has an encoder-decoder structure, which consists of three components: structural encoder, temporal encoder, and decoder. The structural encoder uses relation-aware GCN to capture the topology of each time step in the knowledge graph and generate entity representations at each timestamp. Then, the temporal encoder uses a sequence-based model GRU to integrate the output of the structure encoder and learn historical information at different time scales to generate entity representations that contain structural and temporal features. The decoder is a scoring function, specifically the scoring function of the static KGC method ConvTransE [26], which predicts missing entities or relations in the facts in a conditional probability manner. TAGCN [42] can simultaneously extract structural information and temporal dependency and is also in the architecture of encoder-decoder. It decomposes TKGC into two phases, namely, neighboring temporal message aggregation and entity temporal focus attachment. Accordingly, on the encoder side, a temporal self-attention layer is defined to capture the temporal dependency of the neighboring fact, and a temporal message aggregation module is defined to extract neighborhood structure in TKG and increase the expressiveness of entities. The decoder side chooses the score function of ConvE [23] to estimate the plausibility of the facts. [PERSON] et al. [43] propose TAL-TKGC, which consists of three key components: temporal attention module, TKG embedding module, and completion module. The temporal attention module is designed to capture the deep connection between timestamps, entities, and relations at the semantic level. The TKG embedding module embeds entities using an importance-weighted GCN, which considers both the temporal semantic importance and the structural importance of entities. The completion module uses TTransE [14], which treats the plausibility of facts as the distance between the subject and object entities in the vector space that integrates relations and temporal information. [PERSON] et al. [44] propose a self-supervised approach named THOR (Three-tower grapH cOnvolution networks), which extracts latent knowledge from TKG by jointly exploiting temporal and non-temporal dependencies between entities and structural dependencies between relations. Specifically, they design three-tower GCNs with different roles: Temporal GCN (TempGCN for short), aTemporal GCN (aTempGCN for short), and Relational GCN (RelGCN for short).