A　representative　sample　of　a　social　network　is　essential　for　many　internet　services　that　rely　on　accurate　analysis.　A　good　sampling　method　for　social　network　should　be　able　to　generate　small　sample　network　with　similar　structures　and　distributions　as　its　original　network.　In　this　paper,　a　sampling　algorithm　based　on　graph　partition,　sampling　based　on　graph　partition　(SGP),　is　proposed　to　sample　social　networks.　SGP　firstly　partitions　the　original　network　into　several　sub-networks,　and　then　samples　in　each　sub-network　evenly.　This　procedure　enables　SGP　to　effectively　maintain　the　topological　similarity　and　community　structure　similarity　between　the　sampled　network　and　its　original　network.　Finally,　we　evaluate　SGP　on　several　well-known　datasets.　The　experimental　results　show　that　SGP　method　outperforms　seven　state-of-the-art　methods.　Copyright　©　2019　Inderscience　Enterprises　Ltd.