Earticle

초록

영어

Recently, a graph neural network has played a crucial role across various fields. In this paper, we designed a Graph Convolutional Network (GCN) to analyze population movement at the city level. It consists of four Graph Convolution (GC) layers, with each layer responsible for aggregating knowledge from its neighboring nodes and updating the feature representation for each city. We utilized population mobility data from China, which includes daily city-to-city movement data. GCN estimates the strength of relationships among all cities. Experimental results demonstrate that the proposed GCN achieves improved performance in estimating city-to-city migration flow relationships.

목차

Abstract
1. Introduction
2. Methodology
3. Experiment result
4. Conclusions
Acknowledgment
References

저자

• Muhammad Afaq [ Digital Contents Research Institute, Sejong University ]
• Adnan Hussain [ Digital Contents Research Institute, Sejong University ]
• Hikmat Yar [ Digital Contents Research Institute, Sejong University ]
• Muhammad Munsif [ Digital Contents Research Institute, Sejong University ]
• Min Je Kim [ Digital Contents Research Institute, Sejong University ]
• Sung Wook Baik [ Digital Contents Research Institute, Sejong University ] Corresponding Author

참고문헌