基于多层特征融合与增强的对比图聚类

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Contrastive graph clustering based on multi-level feature fusion andenhancement

Li Zhiming τ1a，1b，1c，2 ，Wei Hepinglat，Zhang Guangkangla，You Dianlong ρ，a，lb，lc，2 （1.a.Schloffotionee&Ein，yoatofofareEgigfberoc，eybofor ComputerVirtalhlog&stmIntegationofHbeiProinceYashnUniersityQiangdaHbei，hina;.S search Institute ofYanshan University，Yanshan University，Shenzhen Guangdong 518o63，China）

Abstract：The majorityofexisting contrastivegraph clustering algorithmsfacethe following issues：theyignorethelow-level featuresand structural informationextracted byshalownetworkswhen generatingnoderepresentation.Thealgorithms neither fullutilizehighorderneighbornodeinformationnorintegrateconfidenceinformationwithtopologicalstructureinformationto construct positive sample pairs.Toaddress theabove issues，thispaper proposed acontrastive graph clustering algorithmbased onmulti-evelfeaturefusionandenhancement.Tealgorithmfirstlyintegratednodefeaturesextractedfromdiferentnetwork layerstoenrichthelow-levelstructural informationofodes.Itthenaggegatednodeinformationthroughthelocaltopolgical correlationsandglobalsemanticsimilaritiesbetweennodestoenhancethecontextualconstraintconsistencyofnoderepresentations.Finaly，combiningconfidenceinformationandtopologicalstructureinformation，thealgorithmconstructedmoreig quality positivesamplepairs to improvetheconsistencyof intra-clusterrepresentation.Theexperimental results showthat CGCMFFEhas excelent performance on four widelyused clustering evaluation metrics.Theoretical analysis and experimental studyunderscoretherucialroleoflow-levelodefeatures，hig-orderneighbornodeinformation，confidence，andtopological structure information in the CGCMFFE algorithm，providing evidence for its superiority.

Key words：multi-level feature fusion；contrastive graph clustering；unsupervised learning

0 引言

深度图聚类是一种利用深度学习将图中节点数据映射到低维稠密向量空间，并以无监督的方式将节点表示划分为若干个不相交簇的技术[1]。（剩余14750字）