Article(id=1263533682871099847, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.02.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742400000000, receivedDateStr=2025-03-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779179505317, onlineDateStr=2026-05-19, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779179505317, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779179505317, creator=13701087609, updateTime=1779179505317, updator=13701087609, issue=Issue{id=1263533678655881533, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='2', pageStart='1', pageEnd='163', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1779179504312, creator=13701087609, updateTime=1779179563167, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263533925629047616, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263533925629047617, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263533678655881533, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=69, endPage=77, ext={EN=ArticleExt(id=1263533684917920205, articleId=1263533682871099847, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Identifying critical segments of an inland waterway network based on Community Bridges, columnId=1240655021705065434, journalTitle=Navigation of China, columnName=Port and Waterway Engineering, runingTitle=null, highlight=null, articleAbstract=

With inland waterways transitioning from linear to networked operation, accurately identifying critical segments is essential for optimizing resource allocation and enhancing system resilience. Existing methods have limitations in effectively identifying segments that play a decisive role in maintaining global connectivity. To address this issue, a community bridge-based method is proposed. Firstly, a weighted topological network is constructed using waterway class and length. Then, the Louvain algorithm is applied to divide the inland waterway network into multiple communities with strong internal connectivity, and edges connecting different communities are identified as critical segments. Finally, attack simulation experiments are conducted to evaluate the effectiveness of the proposed method. Taking the Jiangsu inland waterway network as a case study, the results show a maximum modularity of 0. 901, indicating a pronounced community structure characteristics, and the network can be divided into 18 communities. Currently, 46 critical segments are identified in the network. If all critical segments fail simultaneously, both relative network efficiency and the relative size of the largest connected component decrease by nearly 80%, validating the effectiveness of the identification method. After implementing the 2017—2035 and 2023—2035 waterway network upgrades, the community structure becomes more compact, and the number of identified critical segments decreases while the results remain consistent. The identified critical segments provide theoretical support for routine maintenance and safety supervision of inland waterways, strengthening navigational assurance to enhance network resilience.

, correspAuthors=Peng Liao, 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, authorCompany=null, fund=null, authors=null, authorsList=Wenya Lu, Peng Liao, Muhua Yan), CN=ArticleExt(id=1263533685479956949, articleId=1263533682871099847, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于社团桥的内河航道网关键航段识别方法, columnId=1240655021902197734, journalTitle=中国航海, columnName=水运工程, runingTitle=null, highlight=null, articleAbstract=

随着内河航道由线状运行进入网络化运行新阶段，精准识别网络中的关键航段对于优化资源配置、提升系统抗风险能力至关重要。针对现有方法难以有效识别对全局连通性具有决定性作用航段的问题，提出一种基于社团桥的识别方法。首先，以航道等级和通航里程构建加权拓扑网络；其次，运用Louvain算法将内河航道网划分为多个内部联系紧密的社团，并将连接不同社团的边识别为关键航段；最后，通过攻击仿真试验评估关键航段识别的有效性。以江苏内河航道网为例，计算结果表明其最大模块度为0.901，具有显著的社团结构特征，可以被划分为18个社团。当前该航道网共识别出46条关键航段，若全部失效，相对网络效率和最大连通子图相对大小均下降近80%，验证了关键航段识别方法的有效性。同时，按照2017—2035年、2023—2035年规划航道网对航道等级进行提升，发现社团结构愈加紧凑，关键航段识别数减少且结果具有连贯性。识别出的关键航段可为内河航道的日常维护及安全监管提供理论依据，加强通航保证可提高航道网络韧性。

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鲁文雅（2000—），女，硕士，研究方向为水路交通规划与管理。

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鲁文雅（2000—），女，硕士，研究方向为水路交通规划与管理。

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