A comparative study of network robustness measures
The robustness is an important functionality of networks because it manifests the ability of networks to resist failures or attacks. Many robustness measures have been proposed from different aspects, which provide us various ways to evaluate the network robustness. However, whether these measures c...
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Veröffentlicht in: | Frontiers of Computer Science 2017-08, Vol.11 (4), p.568-584 |
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description | The robustness is an important functionality of networks because it manifests the ability of networks to resist failures or attacks. Many robustness measures have been proposed from different aspects, which provide us various ways to evaluate the network robustness. However, whether these measures can properly evaluate the network robustness and which aspects of network robustness these measures can evaluate are still open questions. Therefore, in this paper, a thorough introduction over attacks and robustness measures is first given, and then nine widely used robustness mea- sures are comparatively studied. To validate whether a robustness measure can evaluate the network robustness properly, the sensitivity of robustness measures is first studied on both initial and optimized networks. Then, the performance of robustness measures in guiding the optimization process is studied, where both the optimization process and the ob- tained optimized networks are studied. The experimental re- suits show that, first, the robustness measures are more sen- sitive to the changes in initial networks than to those in op- timized networks; second, an optimized network may not be useful in practical situations because some useful function- alities, such as the shortest path length and communication efficiency, are sacrificed too much to improve the robustness; third, the robustness of networks in terms of closely corre- lated robustness measures can often be improved together. These results indicate that it is not wise to just apply the opti- mized networks obtained by optimizing over one certain robustness measure into practical situations. Practical requirements should be considered, and optimizing over two or more suitable robustness measures simultaneously is also a promising way. |
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Many robustness measures have been proposed from different aspects, which provide us various ways to evaluate the network robustness. However, whether these measures can properly evaluate the network robustness and which aspects of network robustness these measures can evaluate are still open questions. Therefore, in this paper, a thorough introduction over attacks and robustness measures is first given, and then nine widely used robustness mea- sures are comparatively studied. To validate whether a robustness measure can evaluate the network robustness properly, the sensitivity of robustness measures is first studied on both initial and optimized networks. Then, the performance of robustness measures in guiding the optimization process is studied, where both the optimization process and the ob- tained optimized networks are studied. The experimental re- suits show that, first, the robustness measures are more sen- sitive to the changes in initial networks than to those in op- timized networks; second, an optimized network may not be useful in practical situations because some useful function- alities, such as the shortest path length and communication efficiency, are sacrificed too much to improve the robustness; third, the robustness of networks in terms of closely corre- lated robustness measures can often be improved together. These results indicate that it is not wise to just apply the opti- mized networks obtained by optimizing over one certain robustness measure into practical situations. Practical requirements should be considered, and optimizing over two or more suitable robustness measures simultaneously is also a promising way.</description><identifier>ISSN: 2095-2228</identifier><identifier>EISSN: 2095-2236</identifier><identifier>DOI: 10.1007/s11704-016-6108-z</identifier><language>eng</language><publisher>Beijing: Higher Education Press</publisher><subject>Climbing ; Comparative studies ; Computer Science ; hill climbing algorithm ; malicious attack ; Network topologies ; Networks ; Optimization ; Research Article ; Robustness ; robustness measure ; scale-free network ; 优化网络 ; 优化过程 ; 健壮性 ; 度量方法 ; 网络优化 ; 网络鲁棒性 ; 路径长度 ; 通信效率</subject><ispartof>Frontiers of Computer Science, 2017-08, Vol.11 (4), p.568-584</ispartof><rights>Copyright reserved, 2017, Higher Education Press and Springer-Verlag Berlin Heidelberg</rights><rights>Higher Education Press and Springer-Verlag Berlin Heidelberg 2017</rights><rights>Higher Education Press and Springer-Verlag Berlin Heidelberg 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c392t-72de9b4cd21cdf1c714c217563c64db9783f5c6848ec39a43380d449790ce2e93</citedby><cites>FETCH-LOGICAL-c392t-72de9b4cd21cdf1c714c217563c64db9783f5c6848ec39a43380d449790ce2e93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/71018X/71018X.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11704-016-6108-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918720792?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21387,27923,27924,33743,41487,42556,43804,51318,64384,64388,72240</link.rule.ids></links><search><creatorcontrib>LIU, Jing</creatorcontrib><creatorcontrib>ZHOU, Mingxing</creatorcontrib><creatorcontrib>WANG, Shuai</creatorcontrib><creatorcontrib>LIU, Penghui</creatorcontrib><title>A comparative study of network robustness measures</title><title>Frontiers of Computer Science</title><addtitle>Front. Comput. Sci</addtitle><addtitle>Frontiers of Computer Science in China</addtitle><description>The robustness is an important functionality of networks because it manifests the ability of networks to resist failures or attacks. Many robustness measures have been proposed from different aspects, which provide us various ways to evaluate the network robustness. However, whether these measures can properly evaluate the network robustness and which aspects of network robustness these measures can evaluate are still open questions. Therefore, in this paper, a thorough introduction over attacks and robustness measures is first given, and then nine widely used robustness mea- sures are comparatively studied. To validate whether a robustness measure can evaluate the network robustness properly, the sensitivity of robustness measures is first studied on both initial and optimized networks. Then, the performance of robustness measures in guiding the optimization process is studied, where both the optimization process and the ob- tained optimized networks are studied. The experimental re- suits show that, first, the robustness measures are more sen- sitive to the changes in initial networks than to those in op- timized networks; second, an optimized network may not be useful in practical situations because some useful function- alities, such as the shortest path length and communication efficiency, are sacrificed too much to improve the robustness; third, the robustness of networks in terms of closely corre- lated robustness measures can often be improved together. These results indicate that it is not wise to just apply the opti- mized networks obtained by optimizing over one certain robustness measure into practical situations. Practical requirements should be considered, and optimizing over two or more suitable robustness measures simultaneously is also a promising way.</description><subject>Climbing</subject><subject>Comparative studies</subject><subject>Computer Science</subject><subject>hill climbing algorithm</subject><subject>malicious attack</subject><subject>Network topologies</subject><subject>Networks</subject><subject>Optimization</subject><subject>Research Article</subject><subject>Robustness</subject><subject>robustness measure</subject><subject>scale-free network</subject><subject>优化网络</subject><subject>优化过程</subject><subject>健壮性</subject><subject>度量方法</subject><subject>网络优化</subject><subject>网络鲁棒性</subject><subject>路径长度</subject><subject>通信效率</subject><issn>2095-2228</issn><issn>2095-2236</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kD1PwzAQhi0EElXpD2CLYA7YZye2x6riS6rEArOVOpc2hcapnYDaX4-rVGXrdDe8z308hNwy-sAolY-BMUlFSlme5oyqdH9BRkB1lgLw_PLUg7omkxDWlFKgkGUAIwLTxLpNW_iiq38wCV1f7hJXJQ12v85_Jd4t-tA1GEKywSL0HsMNuaqK74CTYx2Tz-enj9lrOn9_eZtN56nlGrpUQol6IWwJzJYVs5IJC0xmObe5KBdaKl5lNldCYQQKwbmipRBaamoRUPMxuR_mtt5tewydWbveN3GlAc2UBCo1xBQbUta7EDxWpvX1pvA7w6g52DGDHRPtmIMds48MDEyI2WaJ_n_yOUgN0KpertBj2UYXwVTeNV2N_jx6d7xx5ZrlNq48HZlL0PF1Jvkfo0KEdA</recordid><startdate>20170801</startdate><enddate>20170801</enddate><creator>LIU, Jing</creator><creator>ZHOU, Mingxing</creator><creator>WANG, Shuai</creator><creator>LIU, Penghui</creator><general>Higher Education Press</general><general>Springer Nature B.V</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20170801</creationdate><title>A comparative study of network robustness measures</title><author>LIU, Jing ; ZHOU, Mingxing ; WANG, Shuai ; LIU, Penghui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c392t-72de9b4cd21cdf1c714c217563c64db9783f5c6848ec39a43380d449790ce2e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Climbing</topic><topic>Comparative studies</topic><topic>Computer Science</topic><topic>hill climbing algorithm</topic><topic>malicious attack</topic><topic>Network topologies</topic><topic>Networks</topic><topic>Optimization</topic><topic>Research Article</topic><topic>Robustness</topic><topic>robustness measure</topic><topic>scale-free network</topic><topic>优化网络</topic><topic>优化过程</topic><topic>健壮性</topic><topic>度量方法</topic><topic>网络优化</topic><topic>网络鲁棒性</topic><topic>路径长度</topic><topic>通信效率</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>LIU, Jing</creatorcontrib><creatorcontrib>ZHOU, Mingxing</creatorcontrib><creatorcontrib>WANG, Shuai</creatorcontrib><creatorcontrib>LIU, Penghui</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Frontiers of Computer Science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>LIU, Jing</au><au>ZHOU, Mingxing</au><au>WANG, Shuai</au><au>LIU, Penghui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparative study of network robustness measures</atitle><jtitle>Frontiers of Computer Science</jtitle><stitle>Front. Comput. Sci</stitle><addtitle>Frontiers of Computer Science in China</addtitle><date>2017-08-01</date><risdate>2017</risdate><volume>11</volume><issue>4</issue><spage>568</spage><epage>584</epage><pages>568-584</pages><issn>2095-2228</issn><eissn>2095-2236</eissn><abstract>The robustness is an important functionality of networks because it manifests the ability of networks to resist failures or attacks. Many robustness measures have been proposed from different aspects, which provide us various ways to evaluate the network robustness. However, whether these measures can properly evaluate the network robustness and which aspects of network robustness these measures can evaluate are still open questions. Therefore, in this paper, a thorough introduction over attacks and robustness measures is first given, and then nine widely used robustness mea- sures are comparatively studied. To validate whether a robustness measure can evaluate the network robustness properly, the sensitivity of robustness measures is first studied on both initial and optimized networks. Then, the performance of robustness measures in guiding the optimization process is studied, where both the optimization process and the ob- tained optimized networks are studied. The experimental re- suits show that, first, the robustness measures are more sen- sitive to the changes in initial networks than to those in op- timized networks; second, an optimized network may not be useful in practical situations because some useful function- alities, such as the shortest path length and communication efficiency, are sacrificed too much to improve the robustness; third, the robustness of networks in terms of closely corre- lated robustness measures can often be improved together. These results indicate that it is not wise to just apply the opti- mized networks obtained by optimizing over one certain robustness measure into practical situations. Practical requirements should be considered, and optimizing over two or more suitable robustness measures simultaneously is also a promising way.</abstract><cop>Beijing</cop><pub>Higher Education Press</pub><doi>10.1007/s11704-016-6108-z</doi><tpages>17</tpages></addata></record> |
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subjects | Climbing Comparative studies Computer Science hill climbing algorithm malicious attack Network topologies Networks Optimization Research Article Robustness robustness measure scale-free network 优化网络 优化过程 健壮性 度量方法 网络优化 网络鲁棒性 路径长度 通信效率 |
title | A comparative study of network robustness measures |
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