Community　detection　aims　to　identify　dense　subgroups　of　nodes　within　a　network.　However,　in　real-world　networks,　node　attributes　are　often　missing,　making　traditional　methods　less　effective.　In　networks　with　missing　attributes,　the　main　challenge　of　community　detection　is　to　deal　with　the　missing　attribute　information　efficiently　and　use　network　structure　information　to　make　accurate　predictions.　This　article　proposes　an　innovative　method　called　contrastive　sampling-aggregating　transformer　(CSAT)　for　community　detection　in　attribute-missing　networks.　CSAT　incorporates　the　contrastive　learning　principle　to　capture　hidden　patterns　among　nodes　and　to　aggregate　information　from　different　samples　to　create　a　more　robust　and　accurate　methodology　for　community　detection.　Specifically,　CSAT　utilizes　a　sampling　and　propagation　strategy　to　obtain　different　samples　and　smooth　attribute　features　of　the　network　structure　and　leverages　the　Transformer　architecture　to　model　the　pairwise　relationships　between　nodes.　Therefore,　our　method　can　address　the　attribute-missing　issue　by　integrating　the　auxiliary　information　from　both　the　network　structure　and　other　sources.　Extensive　experiments　on　several　benchmark　datasets　demonstrate　CSAT＇s　superior　performance　compared　to　the　state-of-the-art　methods　for　community　detection.