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1、GAIA:GraphClassificationUsingEvolutionaryComputationNingJinCalvinYoungWeiWangUniversityofNorthCarolinaUniversityofNorthCarolinaUniversityofNorthCarolinaatChapelHillatChapelHillatChapelHillChapelHill,NC,USAChapelHill,NC,USAChapelHill,NC,USAnjin@cs.unc.eduyoungc@c
2、s.unc.eduweiwang@cs.unc.eduABSTRACTtobeabletopredictwhichchemicalcompoundsaretoxicandwhichcomponentsarecharacteristicsofchemicaltoxicityDiscriminativesubgraphsarewidelyusedtodefinethefeature[Helma,2004];biologistsareinterestedinstudyingwhichspaceforgraphclassifi
3、cationinlargegraphdatabases.Severalproteinsareabletobindcertainligandsandwhichcanbeusedtoscalableapproacheshavebeenproposedtominediscriminativetreatdiseases[Bandyopadhyay,2006];computerscientistsseektosubgraphs.However,theirintensivecomputationneedspreventfindou
4、thowtolocatebugsinprogramsbyidentifyingthemfrommininglargedatabases.Weproposeanefficientdiscriminativesubgraphsinprogramflowgraphs[Cheng,2009].methodGAIAforminingdiscriminativesubgraphsforgraphPerformingtheseclassificationtasksbyhandisintractableclassificationin
5、largedatabases.Ourmethodemploysanovelcomputationally,thusincreasingattentionhasbeendevotedinsubgraphencodingapproachtosupportanarbitrarysubgraphdevelopinggraphclassificationmethodsinrecentyears.patternexplorationorderandexploresthesubgraphpatternspaceinaprocessr
6、esemblingbiologicalevolution.Inthismanner,1.1RelatedWorkGAIAisabletofinddiscriminativesubgraphpatternsmuchfasterExistingresearchoftenassumesabinarygraphclassificationtaskthanotheralgorithms.Additionally,wetakeadvantageofparallelwhereatargetgraphsetandabackground
7、graphsetaregivenandcomputingtofurtherimprovethequalityofresultingpatterns.Intheobjectiveistoconstructaclassificationmodelfortheend,weemploysequentialcoveragetogenerateassociationdistinguishingthem.Onestraightforwardsolution[Deshpande,rulesasgraphclassifiersusing
8、patternsminedbyGAIA.2005;Bandyopadhyay,2006]tographclassificationisfirstExtensiveexperimentshavebeenperformedtoanalyzethefindingfrequentsubgraphpatterns[Inokuchi,2000