Advanced Network Representation Learning for Container Shipping Network Analysis

被引：2

作者：

Jiang, Liupeng ^{[1
]}

Chen, Lei ^{[2
]}

Wang, Wei ^{[1
]}

Wei, Wei ^{[3
]}

Lv, Zhihan ^{[4
]}

Wang, Heng ^{[5
]}

机构：

[1] Hohai Univ, Coll Harbour Coastal & Offshore Engn, Nanjing, Peoples R China

[2] Hohai Univ, Inst Harbor Coast & Offshore Engn, Nanjing, Peoples R China

[3] Xian Univ Technol, Sch Comp Sci & Engn, Xian, Peoples R China

[4] Qingdao Univ, Qingdao, Peoples R China

[5] Henan Agr Univ, Coll Mech & Elect Engn, Zhengzhou, Peoples R China

来源：

IEEE NETWORK | 2021年 / 35卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Containers; Complex networks; Data mining; Transportation; Big Data; Marine vehicles; Faces;

D O I：

10.1109/MNET.011.2000444

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：182 / 187

页数：6

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