Article(id=1311360869393588712, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762185600000, receivedDateStr=2025-11-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582395169, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582395169, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582395169, creator=13701087609, updateTime=1790582395169, updator=13701087609, issue=Issue{id=1311360859318870426, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='4', pageStart='1', pageEnd='185', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790582392768, creator='13701087609', updateTime=1790583829226, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311366884331716830, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311366884331716831, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=98, endPage=105, ext={EN=ArticleExt(id=1311360870983229930, articleId=1311360869393588712, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Link prediction in global container shipping networks based on a coupled similarity index, columnId=1311360870911926761, journalTitle=Navigation of China, columnName=Marine Traffic Engineering, runingTitle=null, highlight=null, articleAbstract=

Existing link prediction methods for maritime shipping networks primarily rely on single-dimensional network structural information and fail to comprehensively account for multiple influencing factors. To address this limitation, a coupled similarity index integrating multi-level information is proposed. Specifically, to incorporate the effects of practical node attributes such as port operations, hinterland economic conditions, and maritime distances on link prediction, similarity indices based on node realistic attributes are constructed. The predictive performance of 22 individual similarity indices from four perspectives—local network structure, global network structure, quasi-local network structure, and node realistic attributes—is evaluated using the Area Under the Curve (AUC) metric. The index with the highest AUC value from each perspective is then selected. Subsequently, weighted averaging and grid search are employed to determine the optimal weight combination, thereby constructing the coupled similarity index with the highest predictive accuracy. Experimental results on the global container shipping network demonstrate that the Resource Allocation (RA), Random Walk with Restart (RWR), Superposed Random Walk (SRW), and Similarity based on Port Operations (SPO) indices achieve the best prediction performance within their respective perspectives. The proposed coupled similarity index significantly outperforms all individual indices. In particular, the relatively high weight assigned to the SPO index indicates that node realistic attributes play a dominant role in link prediction. Furthermore, comparison with newly added shipping routes in 2024 shows that the proposed method achieves a prediction accuracy of 45.48%, and the spatial distribution of the predicted routes is highly consistent with the global shipping network pattern, thereby validating the effectiveness of the proposed coupled similarity index.

, authors=Yanbin Yang¹, Ling Sun^{2, *}, Zhong Ning¹, authorsList=Yanbin Yang, Ling Sun, Zhong Ning, authorCompany=null, correspAuthors=Ling Sun, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311360871276831211, articleId=1311360869393588712, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=耦合相似性指标下全球集装箱海运网络链路预测, columnId=1240655021902197734, journalTitle=中国航海, columnName=水运工程, runingTitle=null, highlight=null, articleAbstract=

针对现有海运网络链路预测方法多依赖于单一网络结构信息、考虑因素不全面的问题，提出一种融合多层面信息的耦合相似性指标。为充分考虑港口运营、腹地经济及海运距离等现实属性对链路预测的影响，构建节点现实属性层面的相似性指标。利用曲线下面积（AUC）评估4个层面（网络局部结构、全局结构、半局部结构及节点现实属性）共22种单一相似性指标的预测精度，筛选各层面中AUC值最高的指标。采用加权平均与网格搜索法确定其最优的权重组合，进而构建AUC值最高的耦合相似性指标。在全球集装箱海运网络上的试验结果表明，基于资源分配（RA）、重启随机游走（RWR）、叠加局部随机游走（SRW）和港口运营相似性（SPO）指标分别在4个层面中的链路预测精度最优。耦合相似性指标的预测精度显著优于单个指标，其中，SPO的高权重表明节点现实属性在预测中起主导作用。把预测结果与2024年新增航线进行对比发现，该方法预测的准确率达到45.48%，且其空间分布与全球航运格局基本一致，有效验证了所提出耦合相似性指标的有效性。

, authors=杨俨斌¹, 孙领^{2, *}, 宁钟¹, authorsList=杨俨斌, 孙领, 宁钟, authorCompany=null, correspAuthors=孙领, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=EmLUY+SVFQzeMRXc1vU/9Q==, magXml=IlA48ulXdNI+qaB139XzOQ==, pdfUrl=null, pdf=nFXTACkCut5M82pFnfYD/g==, pdfFileSize=1707547, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=DhgEsAx2YLb3iDKYY3VAtA==, mapNumber=null, fund=null)}, authors=[Author(id=1311360871662707187, tenantId=1146029695717560320, journalId=1240618002186551303, articleId=1311360869393588712, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=yanbinyang321@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1311360871738204661, tenantId=1146029695717560320, journalId=1240618002186551303, articleId=1311360869393588712, authorId=1311360871662707187, language=EN, stringName=Yanbin Yang, firstName=Yanbin, middleName=null, lastName=Yang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Management, Fudan University, Shanghai 200433, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1311360871834673654, tenantId=1146029695717560320, journalId=1240618002186551303, articleId=1311360869393588712, authorId=1311360871662707187, language=CN, stringName=杨俨斌, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．复旦大学 管理学院，上海 200433, bio={"content":"

杨俨斌（1994—），男，助理研究员，博士，研究方向为复杂网络、港航物流、一带一路，（E-mail）yanbinyang321@163.com

"}, bioImg=null, bioContent=

杨俨斌（1994—），男，助理研究员，博士，研究方向为复杂网络、港航物流、一带一路，（E-mail）yanbinyang321@163.com

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