Community　structure　detection　in　large-scale　complex　networks　has　been　intensively　investigated　in　recent　years.　In　this　paper,　we　propose　a　new　framework　which　employs　the　ant　colony　clustering　algorithm　based　on　sampling　to　discover　communities　in　large-scale　complex　networks.　The　algorithm　firstly　samples　a　small　number　of　representative　nodes　from　the　large-scale　network;　secondly　it　uses　the　ant　colony　clustering　algorithm　to　cluster　the　sampled　nodes;　thirdly　it　assigns　the　un-sampled　nodes　into　the　detected　communities　according　to　the　similarity　metric;　finally　it　merges　the　initial　clustering　result　to　sustainably　increase　the　modularity　function　value　of　the　detection　results.　A　significant　advantage　of　our　algorithm　is　that　the　sampling　method　greatly　reduces　the　scale　of　the　problem.　Experimental　results　on　computer-generated　and　real-world　networks　show　the　efficiency　of　our　method.　©　2014　IEEE.