# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 7 Conclusions and Future work  
In this work, we propose a new method Multi-Scale Evolutionary Network (MSEN) to solve temporal knowledge graph (TKG) reasoning under the extrapolation setting. First, a hierarchical transfer graph neural network (HT-GNN) is designed in the local memory encoder to acquire rich semantic information from the local knowledge graph, and sequence neural networks are used to learn temporal features of entities and relations. Second, in the global memory encoder, a time-related GNN (TR-GNN) is designed to make full use of temporal information in the global knowledge graph, obtaining periodic and aperiodic temporal-semantic dependencies in the global knowledge graph, providing different weights for entities and relations, improving performance of downstream tasks. Finally, embeddings from different scales are integrated to complete entity prediction. Experimental results on a typical benchmark demonstrate the effectiveness of MSEN in solving TKG reasoning problems.  
While MSEN achieves good results, there remain several promising directions for future investigation: (1) Model Compression: The current MSEN model entails relatively high computational complexity. To enable real-time inference and deployment under resource constraints, we can explore model compression techniques such as pruning and knowledge distillation. By simplifying the model while retaining predictive power, we can enhance its applicability to real-world systems. (2) Robustness to Noisy Data: Historical knowledge graphs often suffer from incomplete, biased, or noisy data. The robustness of MSEN's predictions rely on the quality of the historical inputs. Advanced data filtering, denoising, and imputation methods can be studied to handle imperfect historical graphs. Techniques such as outlier detection and data estimation can help improve the model's resilience against noise and missing facts. (3) Advanced GNN Architectures: Further improving the local and global graph neural network encoders with more sophisticated architectures such as graph attention networks is a promising direction. This can potentially capture more complex structural, semantic, and temporal patterns.  
In the future, we will aim to enhance MSEN along these directions to make broader impacts in real-world applications of temporal knowledge graph reasoning.  
**Author Contributions:** Conceptualization, S.C. ([PERSON]) and Y.Y.; methodology, Y.Y. and R.D.; software, Y.Y. and D.T.; validation, Y.Y., H.X. and D.T.; formal analysis, Y.Y.; investigation, H.X.; writing--original draft preparation, Y.Y.; writing--review and editing, S.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The ICEWS14 and ICEWS18 datasets used in this study are derived from the Integrated Crisis Early Warning System event dataset, which is available from the ICEWS project website ([[https://www.andybeger.com/icews/](https://www.andybeger.com/icews/)]([https://www.andybeger.com/icews/](https://www.andybeger.com/icews/)) (accessed on 27 September 2023)). The specific dataset versions utilized can be accessed through the reference links provided (references [6; 17]).  
**Conflicts of Interest:** The authors declare no conflict of interest.