Analysis of identification methods of key nodes in transportation network
The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification meth...
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Veröffentlicht in: | Chinese physics B 2022-06, Vol.31 (6), p.68905-897 |
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description | The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted
k
-shell degree neighborhood identification method (KSD), the degree
k
-shell identification method (DKS), and the degree
k
-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance. |
doi_str_mv | 10.1088/1674-1056/ac4a6c |
format | Article |
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k
-shell degree neighborhood identification method (KSD), the degree
k
-shell identification method (DKS), and the degree
k
-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.</description><identifier>ISSN: 1674-1056</identifier><identifier>DOI: 10.1088/1674-1056/ac4a6c</identifier><language>eng</language><publisher>Chinese Physical Society and IOP Publishing Ltd</publisher><subject>key node identification ; KSD identification method ; network efficiency ; transportation network</subject><ispartof>Chinese physics B, 2022-06, Vol.31 (6), p.68905-897</ispartof><rights>2022 Chinese Physical Society and IOP Publishing Ltd</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1574-156a641978ae577e80c14e43f1ec5e317d07c01d70f94cff9a01ccd0996d689b3</citedby><cites>FETCH-LOGICAL-c1574-156a641978ae577e80c14e43f1ec5e317d07c01d70f94cff9a01ccd0996d689b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/zgwl-e/zgwl-e.jpg</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1674-1056/ac4a6c/pdf$$EPDF$$P50$$Giop$$H</linktopdf><link.rule.ids>314,780,784,27923,27924,53845</link.rule.ids></links><search><creatorcontrib>Lai, Qiang</creatorcontrib><creatorcontrib>Zhang, Hong-Hao</creatorcontrib><title>Analysis of identification methods of key nodes in transportation network</title><title>Chinese physics B</title><addtitle>Chin. Phys. B</addtitle><description>The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted
k
-shell degree neighborhood identification method (KSD), the degree
k
-shell identification method (DKS), and the degree
k
-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.</description><subject>key node identification</subject><subject>KSD identification method</subject><subject>network efficiency</subject><subject>transportation network</subject><issn>1674-1056</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kL1PwzAQxT2ARCnsjNlYKL1LHDsZq4ovqRILzJaxz8X9sCs7qCp_PQ1BMDGddPfe070fY1cItwhNM0Uh-QShFlNtuBbmhI1-V2fsPOcVgEAoqxF7mgW9OWSfi-gKbyl03nmjOx9DsaXuPdrvy5oORYiWcuFD0SUd8i6mbpAF6vYxrS_YqdObTJc_c8xe7-9e5o-TxfPD03y2mBis-x9qoQXHVjaaaimpAYOceOWQTE0VSgvSAFoJruXGuVYDGmOhbYUVTftWjdn1kLvXwemwVKv4kY4lsvpc7jeKSijLvh0_KmFQmhRzTuTULvmtTgeFoHpQqqeieipqAHW03AwWH3d_wf_KvwAzKGxV</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Lai, Qiang</creator><creator>Zhang, Hong-Hao</creator><general>Chinese Physical Society and IOP Publishing Ltd</general><general>School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China</general><scope>AAYXX</scope><scope>CITATION</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20220601</creationdate><title>Analysis of identification methods of key nodes in transportation network</title><author>Lai, Qiang ; Zhang, Hong-Hao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1574-156a641978ae577e80c14e43f1ec5e317d07c01d70f94cff9a01ccd0996d689b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>key node identification</topic><topic>KSD identification method</topic><topic>network efficiency</topic><topic>transportation network</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lai, Qiang</creatorcontrib><creatorcontrib>Zhang, Hong-Hao</creatorcontrib><collection>CrossRef</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Chinese physics B</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lai, Qiang</au><au>Zhang, Hong-Hao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of identification methods of key nodes in transportation network</atitle><jtitle>Chinese physics B</jtitle><addtitle>Chin. Phys. B</addtitle><date>2022-06-01</date><risdate>2022</risdate><volume>31</volume><issue>6</issue><spage>68905</spage><epage>897</epage><pages>68905-897</pages><issn>1674-1056</issn><abstract>The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted
k
-shell degree neighborhood identification method (KSD), the degree
k
-shell identification method (DKS), and the degree
k
-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.</abstract><pub>Chinese Physical Society and IOP Publishing Ltd</pub><doi>10.1088/1674-1056/ac4a6c</doi><tpages>8</tpages></addata></record> |
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subjects | key node identification KSD identification method network efficiency transportation network |
title | Analysis of identification methods of key nodes in transportation network |
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