# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 1 Introduction  
Temporal knowledge graphs (TKGs) are a type of dynamic knowledge structure in which knowledge may change over time. Compared with traditional static knowledge graphs, TKGs take into account the influence of the time dimension. TKGs with multiple timestamps can be represented as a series of knowledge graph (KG) snapshots, where each snapshot contains all facts within the same timestamp.  
For static knowledge graph completion, methods such as DiSMult [1], ComplEx [2], RGCN [3], ConvE [4], and RotatE [5] have been proposed. TKG reasoning tasks can perform temporal reasoning and evolution analysis of knowledge, mainly divided into interpolation and extrapolation [6]. Interpolation is to complete missing events based on existing facts; extrapolation is to predict future events based on past events. Predicting future events based on the evolution of historical knowledge graphs is very important and challenging. Obtaining possible future events in a timely manner based on temporal relations can support applications such as financial risk control, disaster relief, etc.  
This paper focuses on extrapolation research. The challenge of this task is how to better obtain relevant historical information that can reflect future behaviors. Currently, there are two methods that can be used for TKG extrapolation, namely, query-specific andentire graph-based methods [7]. The former includes RE-NET [6], CyGNet [8], xERTE [9], TITer [10], CENET [11], etc., using high-frequency historical facts related to a query's topics and relations to predict future trend, ignoring structural dependencies within snapshot; the latter includes RE-GCN [12], CEN 7, TANGO [13], TiRGN [14], L2 TKG [15], etc., using historical knowledge graph as input to obtain the evolutionary patterns of TKG.  
The above two methods do not consider the historical features from different perspectives, ignoring potentially useful information. Relying solely on either local or global knowledge graphs may result in information loss, affecting prediction results. However, future events may depend on both local structural dependencies and global temporal patterns. Historical knowledge from varying perspectives likely influences future occurrences to differing degrees.  
In this work, we propose a model to capture multi-scale evolutionary features of historical information, called Multi-Scale Evolutionary Network (MSEN). Typically, different features manifest at different scales, providing diverse semantic information. The local memory focuses on contextual knowledge, while the global memory recalls long-range patterns. By integrating both scales, MSEN can better model TKG evolution for reasoning. The main contributions are:  
1. In the local memory encoder, a hierarchical transfer aware graph neural network (HT-GNN) is proposed to enriches structural semantics within each timestamp;
2. In the global memory encoder, a time related graph neural network (TR-GNN) is proposed to extract periodic and non-periodic temporal patterns globally across timestamps;
3. Through experiments on typical event-based datasets, the paper demonstrates the effectiveness of our proposed model MSEN.