With the increase of international trade activities, the dependence on container shipping is also increasing. The efficiency of container shipping activities will directly affect the trade exchanges between countries. However, traditional network analysis methods face many challenges, such as large-scale, highly dynamic and multi-dimensional issues. To this end, in this article, after reviewing existing network-based analysis methods and their limitations, we introduce the advanced network representation learning technology for container shipping network analysis. To demonstrate the effectiveness of the network representation learning based method, we perform a case study on container shipping network clustering, and the positive results demonstrate the potential of allying network representation learning for container shipping network analysis.
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Advanced Network Representation Learning for Container Shipping Network Analysis
Semantic Scholar · Computer Science · 2021
Abstract
With the increase of international trade activities, the dependence on container shipping is also increasing. The efficiency of container shipping activities will directly affect the trade exchanges between countries. However, traditional network analysis methods face many challenges, such as large-scale, highly dynamic and multi-dimensional issues. To this end, in this article, after reviewing existing network-based analysis methods and their limitations, we introduce the advanced network representation learning technology for container shipping network analysis. To demonstrate the effectiveness of the network representation learning based method, we perform a case study on container shipping network clustering, and the positive results demonstrate the potential of allying network representation learning for container shipping network analysis.