Online　social　networks　have　gained　tremendous　popularity　and　have　dramatically　changed　the　way　we　communicate　in　recent　years.　However,　the　publishing　of　social　network　data　raises　more　and　more　privacy　concerns.　To　protect　user　privacy,　social　networking　data　are　usually　anonymized　before　being　released.　Nevertheless,　existing　anonymization　techniques　do　not　have　sufficient　protection　effects.　A　large　number　of　deanonymization　attacks　have　arisen,　and　they　mainly　make　use　of　either　network　topology　or　node　attribute　information　to　successfully　reidentify　anonymized　users.　In　this　article,　we　model　a　social　network　as　a　structure-attribute　network　(SAN)　integrating　the　structural　characteristics　and　the　attribute　information　associated　with　social　network　users.　A　novel　similarity　measurement　of　social　network　nodes　is　proposed　by　considering　the　structural　similarity　and　attribute　similarity.　A　two-phase　scheme　is　then　designed　to　perform　deanonymization　by　first　dividing　a　social　network　(graph)　into　smaller　subgraphs　based　on　spectral　graph　partitioning　and　then　applying　the　proposed　deanonymization　algorithm　on　each　matched　subgraph　pair.　We　simulate　the　deanonymization　attack　with　extensive　experiments　on　three　real-world　datasets,　and　the　experimental　results　demonstrate　that　our　approach　can　improve　the　accuracy　and　time　complexity　of　deanonymization　compared　with　the　state　of　the　art.